{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:17:50.312264Z",
     "start_time": "2019-10-27T15:17:49.743730Z"
    }
   },
   "outputs": [],
   "source": [
    "from pyUtil import count_change, count_pikes\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-21T18:08:32.457451Z",
     "start_time": "2019-08-21T18:08:32.443452Z"
    }
   },
   "source": [
    "# Data Ingestion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:17:51.812264Z",
     "start_time": "2019-10-27T15:17:51.622385Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"processminer-rare-event-mts-data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:17:52.107088Z",
     "start_time": "2019-10-27T15:17:52.084151Z"
    }
   },
   "outputs": [
    {
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       "      <th>time</th>\n",
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       "      <td>5/1/99 0:02</td>\n",
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       "      <td>-0.138636</td>\n",
       "      <td>-17.836632</td>\n",
       "      <td>0.010803</td>\n",
       "      <td>-0.061114</td>\n",
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       "      <td>10.100265</td>\n",
       "      <td>0.072322</td>\n",
       "      <td>-4.937924</td>\n",
       "      <td>-34.183774</td>\n",
       "      <td>27.004663</td>\n",
       "      <td>3.537487</td>\n",
       "      <td>0.033629</td>\n",
       "      <td>1.840540</td>\n",
       "      <td>0.006090</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>5/1/99 0:06</td>\n",
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       "      <td>-4.758934</td>\n",
       "      <td>-4.023612</td>\n",
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       "      <td>-18.517601</td>\n",
       "      <td>0.002075</td>\n",
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       "      <td>-35.954281</td>\n",
       "      <td>21.672449</td>\n",
       "      <td>3.986095</td>\n",
       "      <td>0.033721</td>\n",
       "      <td>2.554880</td>\n",
       "      <td>0.006097</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5/1/99 0:08</td>\n",
       "      <td>0</td>\n",
       "      <td>0.265578</td>\n",
       "      <td>-4.749928</td>\n",
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       "      <td>0.000732</td>\n",
       "      <td>-0.061114</td>\n",
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       "<p>5 rows × 63 columns</p>\n",
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      ],
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       "          time  y        x1        x2        x3         x4        x5  \\\n",
       "0  5/1/99 0:00  0  0.376665 -4.596435 -4.095756  13.497687 -0.118830   \n",
       "1  5/1/99 0:02  0  0.475720 -4.542502 -4.018359  16.230659 -0.128733   \n",
       "2  5/1/99 0:04  0  0.363848 -4.681394 -4.353147  14.127997 -0.138636   \n",
       "3  5/1/99 0:06  0  0.301590 -4.758934 -4.023612  13.161566 -0.148142   \n",
       "4  5/1/99 0:08  0  0.265578 -4.749928 -4.333150  15.267340 -0.155314   \n",
       "\n",
       "          x6        x7        x8  ...        x52       x53       x54  \\\n",
       "0 -20.669883  0.000732 -0.061114  ...  10.091721  0.053279 -4.936434   \n",
       "1 -18.758079  0.000732 -0.061114  ...  10.095871  0.062801 -4.937179   \n",
       "2 -17.836632  0.010803 -0.061114  ...  10.100265  0.072322 -4.937924   \n",
       "3 -18.517601  0.002075 -0.061114  ...  10.104660  0.081600 -4.938669   \n",
       "4 -17.505913  0.000732 -0.061114  ...  10.109054  0.091121 -4.939414   \n",
       "\n",
       "         x55        x56       x57       x58       x59       x60  x61  \n",
       "0 -24.590146  18.515436  3.473400  0.033444  0.953219  0.006076    0  \n",
       "1 -32.413266  22.760065  2.682933  0.033536  1.090502  0.006083    0  \n",
       "2 -34.183774  27.004663  3.537487  0.033629  1.840540  0.006090    0  \n",
       "3 -35.954281  21.672449  3.986095  0.033721  2.554880  0.006097    0  \n",
       "4 -37.724789  21.907251  3.601573  0.033777  1.410494  0.006105    0  \n",
       "\n",
       "[5 rows x 63 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:17:52.658627Z",
     "start_time": "2019-10-27T15:17:52.472113Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th>x1</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>18274.0</td>\n",
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       "      <td>18274.000000</td>\n",
       "      <td>18274.000000</td>\n",
       "      <td>18274.000000</td>\n",
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       "      <td>18274.000000</td>\n",
       "      <td>18274.000000</td>\n",
       "      <td>18274.000000</td>\n",
       "      <td>18274.000000</td>\n",
       "      <td>18274.0</td>\n",
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       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.011198</td>\n",
       "      <td>0.194998</td>\n",
       "      <td>0.626211</td>\n",
       "      <td>-10.139593</td>\n",
       "      <td>0.006840</td>\n",
       "      <td>2.489880</td>\n",
       "      <td>0.001483</td>\n",
       "      <td>-0.004143</td>\n",
       "      <td>-0.003368</td>\n",
       "      <td>...</td>\n",
       "      <td>0.401567</td>\n",
       "      <td>0.458541</td>\n",
       "      <td>0.183983</td>\n",
       "      <td>2.454428</td>\n",
       "      <td>9.401764</td>\n",
       "      <td>0.236905</td>\n",
       "      <td>-0.001881</td>\n",
       "      <td>-0.069369</td>\n",
       "      <td>0.001252</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.741833</td>\n",
       "      <td>4.912832</td>\n",
       "      <td>5.898330</td>\n",
       "      <td>130.983772</td>\n",
       "      <td>0.633845</td>\n",
       "      <td>36.987546</td>\n",
       "      <td>0.108664</td>\n",
       "      <td>0.075172</td>\n",
       "      <td>0.155036</td>\n",
       "      <td>...</td>\n",
       "      <td>6.052476</td>\n",
       "      <td>4.612143</td>\n",
       "      <td>3.023366</td>\n",
       "      <td>67.891696</td>\n",
       "      <td>81.289542</td>\n",
       "      <td>2.324591</td>\n",
       "      <td>0.048760</td>\n",
       "      <td>10.428501</td>\n",
       "      <td>0.004718</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.787279</td>\n",
       "      <td>-17.316550</td>\n",
       "      <td>-18.198509</td>\n",
       "      <td>-322.781610</td>\n",
       "      <td>-1.623988</td>\n",
       "      <td>-279.408440</td>\n",
       "      <td>-0.429273</td>\n",
       "      <td>-0.451141</td>\n",
       "      <td>-0.120087</td>\n",
       "      <td>...</td>\n",
       "      <td>-16.555008</td>\n",
       "      <td>-14.211369</td>\n",
       "      <td>-8.210370</td>\n",
       "      <td>-230.574030</td>\n",
       "      <td>-269.039500</td>\n",
       "      <td>-4.888661</td>\n",
       "      <td>-0.149790</td>\n",
       "      <td>-100.810500</td>\n",
       "      <td>-0.012229</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.405828</td>\n",
       "      <td>-2.123012</td>\n",
       "      <td>-3.477843</td>\n",
       "      <td>-111.428897</td>\n",
       "      <td>-0.446864</td>\n",
       "      <td>-23.687049</td>\n",
       "      <td>-0.058658</td>\n",
       "      <td>-0.050880</td>\n",
       "      <td>-0.059966</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.672684</td>\n",
       "      <td>-1.933476</td>\n",
       "      <td>0.492208</td>\n",
       "      <td>-39.882046</td>\n",
       "      <td>-45.399116</td>\n",
       "      <td>-1.598804</td>\n",
       "      <td>0.000473</td>\n",
       "      <td>0.295016</td>\n",
       "      <td>-0.001808</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.128147</td>\n",
       "      <td>-0.060635</td>\n",
       "      <td>-0.161470</td>\n",
       "      <td>-14.940988</td>\n",
       "      <td>-0.120699</td>\n",
       "      <td>10.585686</td>\n",
       "      <td>-0.009338</td>\n",
       "      <td>-0.000993</td>\n",
       "      <td>-0.030057</td>\n",
       "      <td>...</td>\n",
       "      <td>0.294846</td>\n",
       "      <td>0.144222</td>\n",
       "      <td>0.712452</td>\n",
       "      <td>17.633121</td>\n",
       "      <td>1.682657</td>\n",
       "      <td>0.085826</td>\n",
       "      <td>0.012867</td>\n",
       "      <td>0.733812</td>\n",
       "      <td>0.000704</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.420588</td>\n",
       "      <td>2.335692</td>\n",
       "      <td>3.443554</td>\n",
       "      <td>92.052390</td>\n",
       "      <td>0.326277</td>\n",
       "      <td>32.206184</td>\n",
       "      <td>0.060506</td>\n",
       "      <td>0.038986</td>\n",
       "      <td>0.001255</td>\n",
       "      <td>...</td>\n",
       "      <td>5.132737</td>\n",
       "      <td>3.237362</td>\n",
       "      <td>2.676296</td>\n",
       "      <td>44.113162</td>\n",
       "      <td>63.318964</td>\n",
       "      <td>2.222118</td>\n",
       "      <td>0.020988</td>\n",
       "      <td>1.266258</td>\n",
       "      <td>0.004064</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.054156</td>\n",
       "      <td>16.742105</td>\n",
       "      <td>15.900116</td>\n",
       "      <td>334.694098</td>\n",
       "      <td>2.371770</td>\n",
       "      <td>96.060768</td>\n",
       "      <td>1.705590</td>\n",
       "      <td>0.553834</td>\n",
       "      <td>4.060033</td>\n",
       "      <td>...</td>\n",
       "      <td>14.180588</td>\n",
       "      <td>11.148006</td>\n",
       "      <td>6.637265</td>\n",
       "      <td>287.252017</td>\n",
       "      <td>252.147455</td>\n",
       "      <td>6.922008</td>\n",
       "      <td>0.067249</td>\n",
       "      <td>6.985460</td>\n",
       "      <td>0.020510</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             y            x1            x2            x3            x4  \\\n",
       "count  18274.0  18274.000000  18274.000000  18274.000000  18274.000000   \n",
       "mean       0.0      0.011198      0.194998      0.626211    -10.139593   \n",
       "std        0.0      0.741833      4.912832      5.898330    130.983772   \n",
       "min        0.0     -3.787279    -17.316550    -18.198509   -322.781610   \n",
       "25%        0.0     -0.405828     -2.123012     -3.477843   -111.428897   \n",
       "50%        0.0      0.128147     -0.060635     -0.161470    -14.940988   \n",
       "75%        0.0      0.420588      2.335692      3.443554     92.052390   \n",
       "max        0.0      3.054156     16.742105     15.900116    334.694098   \n",
       "\n",
       "                 x5            x6            x7            x8            x9  \\\n",
       "count  18274.000000  18274.000000  18274.000000  18274.000000  18274.000000   \n",
       "mean       0.006840      2.489880      0.001483     -0.004143     -0.003368   \n",
       "std        0.633845     36.987546      0.108664      0.075172      0.155036   \n",
       "min       -1.623988   -279.408440     -0.429273     -0.451141     -0.120087   \n",
       "25%       -0.446864    -23.687049     -0.058658     -0.050880     -0.059966   \n",
       "50%       -0.120699     10.585686     -0.009338     -0.000993     -0.030057   \n",
       "75%        0.326277     32.206184      0.060506      0.038986      0.001255   \n",
       "max        2.371770     96.060768      1.705590      0.553834      4.060033   \n",
       "\n",
       "       ...           x52           x53           x54           x55  \\\n",
       "count  ...  18274.000000  18274.000000  18274.000000  18274.000000   \n",
       "mean   ...      0.401567      0.458541      0.183983      2.454428   \n",
       "std    ...      6.052476      4.612143      3.023366     67.891696   \n",
       "min    ...    -16.555008    -14.211369     -8.210370   -230.574030   \n",
       "25%    ...     -3.672684     -1.933476      0.492208    -39.882046   \n",
       "50%    ...      0.294846      0.144222      0.712452     17.633121   \n",
       "75%    ...      5.132737      3.237362      2.676296     44.113162   \n",
       "max    ...     14.180588     11.148006      6.637265    287.252017   \n",
       "\n",
       "                x56           x57           x58           x59           x60  \\\n",
       "count  18274.000000  18274.000000  18274.000000  18274.000000  18274.000000   \n",
       "mean       9.401764      0.236905     -0.001881     -0.069369      0.001252   \n",
       "std       81.289542      2.324591      0.048760     10.428501      0.004718   \n",
       "min     -269.039500     -4.888661     -0.149790   -100.810500     -0.012229   \n",
       "25%      -45.399116     -1.598804      0.000473      0.295016     -0.001808   \n",
       "50%        1.682657      0.085826      0.012867      0.733812      0.000704   \n",
       "75%       63.318964      2.222118      0.020988      1.266258      0.004064   \n",
       "max      252.147455      6.922008      0.067249      6.985460      0.020510   \n",
       "\n",
       "           x61  \n",
       "count  18274.0  \n",
       "mean       0.0  \n",
       "std        0.0  \n",
       "min        0.0  \n",
       "25%        0.0  \n",
       "50%        0.0  \n",
       "75%        0.0  \n",
       "max        0.0  \n",
       "\n",
       "[8 rows x 62 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.y==0].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Althought x61 has a small std it has some information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:17:53.202703Z",
     "start_time": "2019-10-27T15:17:53.048080Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['x60'].loc[df.y==0].hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The purpose is to create an alert until 6 minutes before the break and eliminate the freezed feature (x61). Data is acquired every 2min, so the region created will consist of 3 samples. Furthermore, to avoid errors while creating the region all the working zones with less than 3 samples will be dropped. \n",
    "\n",
    "Some hightlights: the time features were eliminated since the period of data (less than a month) do note justify creating features like (hour, day, shift, quarter, day of week, etc.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:17:56.115487Z",
     "start_time": "2019-10-27T15:17:53.924351Z"
    },
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "target = 'y'\n",
    "# Creating 3 time shifts to build the region before break\n",
    "df['y_1'] = df['y'].shift(-1)\n",
    "df['y_2'] = df['y'].shift(-2)\n",
    "df['y_3'] = df['y'].shift(-3)\n",
    "\n",
    "# First generated feature\n",
    "df = count_change(df,'y','count_y')\n",
    "\n",
    "# Creates an ID for each counter reset\n",
    "df = count_pikes(df,'y','count_id', True)\n",
    "# ID Value adjustment - Starts at 0\n",
    "df['count_id'] = df['count_id'] - df['count_id'].min()\n",
    "\n",
    "# Deleting the downtime events and the working zones with less than 6min\n",
    "df_grouped = df.groupby('count_id').max()[['count_y', target]]\n",
    "ids = df_grouped.loc[(df_grouped[target] == 0) & (df_grouped['count_y'] >= 3)].index\n",
    "df = df.loc[df['count_id'].isin(ids)]\n",
    "\n",
    "# Building the region before the break\n",
    "df['y'] = df.apply(lambda x: 1 if ((x['y_1'] == 1) | (x['y_2'] == 1) | (x['y_3'] == 1)) else 0, axis=1) \n",
    "\n",
    "# Removing target and unused columns\n",
    "features = df.columns.tolist()\n",
    "features.remove('time') # Unused column\n",
    "features.remove('y_1')      # Aux columns\n",
    "features.remove('y_2')      # ''\n",
    "features.remove('y_3')      # ''\n",
    "features.remove('count_id') # ''\n",
    "features.remove('x61') # Freezed variable\n",
    "features.remove(target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:17:56.202259Z",
     "start_time": "2019-10-27T15:17:56.198227Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['x1', 'x2', 'x3', 'x4', 'x5', 'x6', 'x7', 'x8', 'x9', 'x10', 'x11', 'x12', 'x13', 'x14', 'x15', 'x16', 'x17', 'x18', 'x19', 'x20', 'x21', 'x22', 'x23', 'x24', 'x25', 'x26', 'x27', 'x28', 'x29', 'x30', 'x31', 'x32', 'x33', 'x34', 'x35', 'x36', 'x37', 'x38', 'x39', 'x40', 'x41', 'x42', 'x43', 'x44', 'x45', 'x46', 'x47', 'x48', 'x49', 'x50', 'x51', 'x52', 'x53', 'x54', 'x55', 'x56', 'x57', 'x58', 'x59', 'x60', 'count_y']\n"
     ]
    }
   ],
   "source": [
    "print(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:17:56.298002Z",
     "start_time": "2019-10-27T15:17:56.292973Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.019548789836819624"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Minority class percentage\n",
    "df[target].value_counts()[1] / df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:17:56.414647Z",
     "start_time": "2019-10-27T15:17:56.388717Z"
    }
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>x1</th>\n",
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       "      <th>x58</th>\n",
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       "      <th>x60</th>\n",
       "      <th>count_y</th>\n",
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       "      <td>0</td>\n",
       "      <td>0.376665</td>\n",
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       "      <td>-0.059966</td>\n",
       "      <td>-0.038189</td>\n",
       "      <td>...</td>\n",
       "      <td>10.091721</td>\n",
       "      <td>0.053279</td>\n",
       "      <td>-4.936434</td>\n",
       "      <td>-24.590146</td>\n",
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       "      <td>0.475720</td>\n",
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       "      <td>-4.353147</td>\n",
       "      <td>14.127997</td>\n",
       "      <td>-0.138636</td>\n",
       "      <td>-17.836632</td>\n",
       "      <td>0.010803</td>\n",
       "      <td>-0.061114</td>\n",
       "      <td>-0.030057</td>\n",
       "      <td>-0.018352</td>\n",
       "      <td>...</td>\n",
       "      <td>10.100265</td>\n",
       "      <td>0.072322</td>\n",
       "      <td>-4.937924</td>\n",
       "      <td>-34.183774</td>\n",
       "      <td>27.004663</td>\n",
       "      <td>3.537487</td>\n",
       "      <td>0.033629</td>\n",
       "      <td>1.840540</td>\n",
       "      <td>0.006090</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.301590</td>\n",
       "      <td>-4.758934</td>\n",
       "      <td>-4.023612</td>\n",
       "      <td>13.161566</td>\n",
       "      <td>-0.148142</td>\n",
       "      <td>-18.517601</td>\n",
       "      <td>0.002075</td>\n",
       "      <td>-0.061114</td>\n",
       "      <td>-0.019986</td>\n",
       "      <td>-0.008280</td>\n",
       "      <td>...</td>\n",
       "      <td>10.104660</td>\n",
       "      <td>0.081600</td>\n",
       "      <td>-4.938669</td>\n",
       "      <td>-35.954281</td>\n",
       "      <td>21.672449</td>\n",
       "      <td>3.986095</td>\n",
       "      <td>0.033721</td>\n",
       "      <td>2.554880</td>\n",
       "      <td>0.006097</td>\n",
       "      <td>3</td>\n",
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       "      <td>4</td>\n",
       "      <td>0.265578</td>\n",
       "      <td>-4.749928</td>\n",
       "      <td>-4.333150</td>\n",
       "      <td>15.267340</td>\n",
       "      <td>-0.155314</td>\n",
       "      <td>-17.505913</td>\n",
       "      <td>0.000732</td>\n",
       "      <td>-0.061114</td>\n",
       "      <td>-0.030057</td>\n",
       "      <td>-0.008280</td>\n",
       "      <td>...</td>\n",
       "      <td>10.109054</td>\n",
       "      <td>0.091121</td>\n",
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       "      <td>3.601573</td>\n",
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       "      <td>1.410494</td>\n",
       "      <td>0.006105</td>\n",
       "      <td>4</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         x1        x2        x3         x4        x5         x6        x7  \\\n",
       "0  0.376665 -4.596435 -4.095756  13.497687 -0.118830 -20.669883  0.000732   \n",
       "1  0.475720 -4.542502 -4.018359  16.230659 -0.128733 -18.758079  0.000732   \n",
       "2  0.363848 -4.681394 -4.353147  14.127997 -0.138636 -17.836632  0.010803   \n",
       "3  0.301590 -4.758934 -4.023612  13.161566 -0.148142 -18.517601  0.002075   \n",
       "4  0.265578 -4.749928 -4.333150  15.267340 -0.155314 -17.505913  0.000732   \n",
       "\n",
       "         x8        x9       x10  ...        x52       x53       x54  \\\n",
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       "3 -0.061114 -0.019986 -0.008280  ...  10.104660  0.081600 -4.938669   \n",
       "4 -0.061114 -0.030057 -0.008280  ...  10.109054  0.091121 -4.939414   \n",
       "\n",
       "         x55        x56       x57       x58       x59       x60  count_y  \n",
       "0 -24.590146  18.515436  3.473400  0.033444  0.953219  0.006076        0  \n",
       "1 -32.413266  22.760065  2.682933  0.033536  1.090502  0.006083        1  \n",
       "2 -34.183774  27.004663  3.537487  0.033629  1.840540  0.006090        2  \n",
       "3 -35.954281  21.672449  3.986095  0.033721  2.554880  0.006097        3  \n",
       "4 -37.724789  21.907251  3.601573  0.033777  1.410494  0.006105        4  \n",
       "\n",
       "[5 rows x 61 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[features].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:17:56.514423Z",
     "start_time": "2019-10-27T15:17:56.502461Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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      ],
      "text/plain": [
       "     y  count_y  count_id\n",
       "255  0      255         0\n",
       "256  1      256         0\n",
       "257  1      257         0\n",
       "258  1      258         0\n",
       "260  0        0         2\n",
       "261  0        1         2\n",
       "262  0        2         2\n",
       "263  0        3         2"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['y','count_y', 'count_id']].iloc[255:263]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Manipulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-22T12:43:01.709876Z",
     "start_time": "2019-08-22T12:43:01.702882Z"
    }
   },
   "source": [
    "The first feature created was the counter variable, because it contextualizes anomalies and explains the other features condition through time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step is to create the RUL variable, and for that we will use the column count_id created previously to obtain the maximum RUL value from the column count_y."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:17:57.511130Z",
     "start_time": "2019-10-27T15:17:56.673955Z"
    }
   },
   "outputs": [],
   "source": [
    "max_rul = df.groupby('count_id').max()['count_y'].reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:17:57.618430Z",
     "start_time": "2019-10-27T15:17:57.613452Z"
    }
   },
   "outputs": [],
   "source": [
    "max_rul.rename(columns={\"count_y\":\"MAX_RUL\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:17:57.717164Z",
     "start_time": "2019-10-27T15:17:57.709187Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count_id</th>\n",
       "      <th>MAX_RUL</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "   count_id  MAX_RUL\n",
       "0         0      258\n",
       "1         2      515\n",
       "2         4       57\n",
       "3         6      199\n",
       "4         8      225"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_rul.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will carry the MAX_RUL information to the main dataframe through a left outer join. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:17:58.237820Z",
     "start_time": "2019-10-27T15:17:58.197914Z"
    }
   },
   "outputs": [],
   "source": [
    "# Left join on the count_id\n",
    "df = pd.merge(df,max_rul,how='left',on='count_id')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The RUL is calculated by the difference between tha maximum RUL and the current counter value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:17:59.026539Z",
     "start_time": "2019-10-27T15:17:59.021502Z"
    }
   },
   "outputs": [],
   "source": [
    "df['RUL'] = df['MAX_RUL'] - df['count_y']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we have calculated the RUL we can set it as the new target for the problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:17:59.653662Z",
     "start_time": "2019-10-27T15:17:59.650671Z"
    }
   },
   "outputs": [],
   "source": [
    "# Setting the new target\n",
    "target = 'RUL'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:17:59.952161Z",
     "start_time": "2019-10-27T15:17:59.928199Z"
    }
   },
   "outputs": [
    {
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       "      <td>257</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>258</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>258</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>259</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>260</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>261</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>262</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>512</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     y  count_id  count_y  RUL\n",
       "255  0         0      255    3\n",
       "256  1         0      256    2\n",
       "257  1         0      257    1\n",
       "258  1         0      258    0\n",
       "259  0         2        0  515\n",
       "260  0         2        1  514\n",
       "261  0         2        2  513\n",
       "262  0         2        3  512"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['y', 'count_id', 'count_y', 'RUL']].iloc[255:263]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:18:01.071933Z",
     "start_time": "2019-10-27T15:18:00.659645Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:18:01.160265Z",
     "start_time": "2019-10-27T15:18:01.157274Z"
    }
   },
   "outputs": [],
   "source": [
    "timesteps = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:18:01.423623Z",
     "start_time": "2019-10-27T15:18:01.265028Z"
    }
   },
   "outputs": [],
   "source": [
    "# List of shifted dataframes according to the number of timesteps\n",
    "df_list = [df[features].shift(shift_val) if (shift_val == 0) \n",
    "                                else df[features].shift(-shift_val).add_suffix(f'_{shift_val}') \n",
    "                                for shift_val in range(0,timesteps)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:18:01.741123Z",
     "start_time": "2019-10-27T15:18:01.712789Z"
    }
   },
   "outputs": [],
   "source": [
    "# Concatenating list\n",
    "df_concat = pd.concat(df_list, axis=1, sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:18:02.114755Z",
     "start_time": "2019-10-27T15:18:02.057879Z"
    }
   },
   "outputs": [],
   "source": [
    "df_concat = df_concat.iloc[:-timesteps,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:18:02.483074Z",
     "start_time": "2019-10-27T15:18:02.440842Z"
    }
   },
   "outputs": [],
   "source": [
    "# Default train_test_split - test_size=0.25\n",
    "x_train, x_test, y_train, y_test = train_test_split(df_concat, df[target].iloc[:-timesteps], random_state=10, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Standardizing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given the scale sensitivity of Recurrent Neural Networks, such as LSTM that is used in this problem, it is necessary to standardize the data. So we will use the Standard Scaler from the Scikit-Learn library  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:18:03.657586Z",
     "start_time": "2019-10-27T15:18:03.653597Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:18:04.097411Z",
     "start_time": "2019-10-27T15:18:04.093421Z"
    }
   },
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:18:04.703017Z",
     "start_time": "2019-10-27T15:18:04.512302Z"
    }
   },
   "outputs": [],
   "source": [
    "# Scaling and transforming the array back to the dataframe format\n",
    "# Training\n",
    "x_train_lstm = pd.DataFrame(data=scaler.fit_transform(x_train), columns=x_train.columns)\n",
    "# Test\n",
    "x_test_lstm = pd.DataFrame(data=scaler.transform(x_test), columns=x_test.columns) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The approach is to use LSTM with Keras and Tensorflow. The imbalanced classes are a challenge\n",
    "for this type of algorithm. The proposed idea is to transform it into a regression problem to determine the \n",
    "RUL (Remaining Useful Life) of the failure. The use of LSTM in this type of problem is more challeging regarding it's domain but it will fix the issue of imbalanced classes. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is necessary some work with the dataframe to define this problem:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1 -** First we need to format the data shape so it can fit into the LSTM's required format: [samples, timesteps, features] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Format used [13696 samples, 5 timesteps(10 minutes), 61 features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:18:06.198789Z",
     "start_time": "2019-10-27T15:18:06.192806Z"
    }
   },
   "outputs": [],
   "source": [
    "def to_3D(X, features, timesteps=5):\n",
    "    '''\n",
    "        Shapes the dataset so it can fit into LSTM's format requirement.\n",
    "    '''\n",
    "    # Creating an empty tridimensional array\n",
    "    X_trans = np.empty((X.shape[0], timesteps, 0))\n",
    "\n",
    "    # Adjusting the shape of the data\n",
    "    for feat in features:\n",
    "        # Regular expressions to filter each feature and\n",
    "        # drop the NaN values generated from the shift\n",
    "        df_filtered = X.filter(regex=f'{feat}(_|$)')\n",
    "        df_filtered = df_filtered.values.reshape(df_filtered.shape[0], timesteps, 1)\n",
    "        X_trans = np.append(X_trans, df_filtered, axis=2)\n",
    "    \n",
    "    return X_trans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building RNN architecutre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:18:09.302539Z",
     "start_time": "2019-10-27T15:18:07.779670Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "c:\\users\\danie\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "c:\\users\\danie\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "c:\\users\\danie\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "c:\\users\\danie\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "c:\\users\\danie\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "c:\\users\\danie\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
      "c:\\users\\danie\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "c:\\users\\danie\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "c:\\users\\danie\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "c:\\users\\danie\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "c:\\users\\danie\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "c:\\users\\danie\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential, load_model\n",
    "from keras.layers import Dense, Dropout, LSTM\n",
    "import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:18:10.138332Z",
     "start_time": "2019-10-27T15:18:09.801235Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From c:\\users\\danie\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\users\\danie\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\users\\danie\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\users\\danie\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:148: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\users\\danie\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3733: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
      "WARNING:tensorflow:From c:\\users\\danie\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\keras\\optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n",
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_1 (LSTM)                (None, 5, 15)             4620      \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 5, 15)             0         \n",
      "_________________________________________________________________\n",
      "lstm_2 (LSTM)                (None, 10)                1040      \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 10)                0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 11        \n",
      "=================================================================\n",
      "Total params: 5,671\n",
      "Trainable params: 5,671\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(input_shape=(timesteps, len(features)), units=15, return_sequences=True))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(LSTM(input_shape=(timesteps,len(features)), units=10, return_sequences=False))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(units=1, activation = 'relu'))\n",
    "model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-19T03:19:47.209013Z",
     "start_time": "2019-09-19T01:07:47.335994Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From c:\\users\\danie\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow\\python\\ops\\math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "WARNING:tensorflow:From c:\\users\\danie\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
      "\n",
      "Train on 10953 samples, validate on 2739 samples\n",
      "Epoch 1/1000\n",
      "10953/10953 [==============================] - 9s 790us/step - loss: 52635.7738 - val_loss: 48278.8369\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 48278.83692, saving model to lstm_model.h5\n",
      "Epoch 2/1000\n",
      "10953/10953 [==============================] - 8s 698us/step - loss: 48095.0048 - val_loss: 44321.1886\n",
      "\n",
      "Epoch 00002: val_loss improved from 48278.83692 to 44321.18856, saving model to lstm_model.h5\n",
      "Epoch 3/1000\n",
      "10953/10953 [==============================] - 8s 743us/step - loss: 44260.4959 - val_loss: 40794.1371\n",
      "\n",
      "Epoch 00003: val_loss improved from 44321.18856 to 40794.13708, saving model to lstm_model.h5\n",
      "Epoch 4/1000\n",
      "10953/10953 [==============================] - 9s 856us/step - loss: 40797.0277 - val_loss: 37646.2791\n",
      "\n",
      "Epoch 00004: val_loss improved from 40794.13708 to 37646.27910, saving model to lstm_model.h5\n",
      "Epoch 5/1000\n",
      "10953/10953 [==============================] - 9s 815us/step - loss: 37810.7148 - val_loss: 34827.9817\n",
      "\n",
      "Epoch 00005: val_loss improved from 37646.27910 to 34827.98166, saving model to lstm_model.h5\n",
      "Epoch 6/1000\n",
      "10953/10953 [==============================] - 9s 851us/step - loss: 35027.5251 - val_loss: 32283.3219\n",
      "\n",
      "Epoch 00006: val_loss improved from 34827.98166 to 32283.32194, saving model to lstm_model.h5\n",
      "Epoch 7/1000\n",
      "10953/10953 [==============================] - 10s 871us/step - loss: 32568.9632 - val_loss: 29986.1944\n",
      "\n",
      "Epoch 00007: val_loss improved from 32283.32194 to 29986.19436, saving model to lstm_model.h5\n",
      "Epoch 8/1000\n",
      "10953/10953 [==============================] - 9s 795us/step - loss: 30193.1796 - val_loss: 27767.7793\n",
      "\n",
      "Epoch 00008: val_loss improved from 29986.19436 to 27767.77925, saving model to lstm_model.h5\n",
      "Epoch 9/1000\n",
      "10953/10953 [==============================] - 9s 801us/step - loss: 28084.4068 - val_loss: 25823.0632\n",
      "\n",
      "Epoch 00009: val_loss improved from 27767.77925 to 25823.06319, saving model to lstm_model.h5\n",
      "Epoch 10/1000\n",
      "10953/10953 [==============================] - 9s 865us/step - loss: 26146.6708 - val_loss: 23928.1415\n",
      "\n",
      "Epoch 00010: val_loss improved from 25823.06319 to 23928.14153, saving model to lstm_model.h5\n",
      "Epoch 11/1000\n",
      "10953/10953 [==============================] - 9s 779us/step - loss: 24241.1020 - val_loss: 21996.8220\n",
      "\n",
      "Epoch 00011: val_loss improved from 23928.14153 to 21996.82202, saving model to lstm_model.h5\n",
      "Epoch 12/1000\n",
      "10953/10953 [==============================] - 9s 831us/step - loss: 22345.7467 - val_loss: 20098.9925\n",
      "\n",
      "Epoch 00012: val_loss improved from 21996.82202 to 20098.99249, saving model to lstm_model.h5\n",
      "Epoch 13/1000\n",
      "10953/10953 [==============================] - 9s 843us/step - loss: 20703.8405 - val_loss: 18554.0029\n",
      "\n",
      "Epoch 00013: val_loss improved from 20098.99249 to 18554.00293, saving model to lstm_model.h5\n",
      "Epoch 14/1000\n",
      "10953/10953 [==============================] - 9s 858us/step - loss: 19038.5452 - val_loss: 17138.5765\n",
      "\n",
      "Epoch 00014: val_loss improved from 18554.00293 to 17138.57645, saving model to lstm_model.h5\n",
      "Epoch 15/1000\n",
      "10953/10953 [==============================] - 9s 823us/step - loss: 17535.4017 - val_loss: 15485.7873\n",
      "\n",
      "Epoch 00015: val_loss improved from 17138.57645 to 15485.78725, saving model to lstm_model.h5\n",
      "Epoch 16/1000\n",
      "10953/10953 [==============================] - 9s 794us/step - loss: 16085.3610 - val_loss: 14248.0254\n",
      "\n",
      "Epoch 00016: val_loss improved from 15485.78725 to 14248.02542, saving model to lstm_model.h5\n",
      "Epoch 17/1000\n",
      "10953/10953 [==============================] - 9s 798us/step - loss: 14850.3556 - val_loss: 13083.2849\n",
      "\n",
      "Epoch 00017: val_loss improved from 14248.02542 to 13083.28488, saving model to lstm_model.h5\n",
      "Epoch 18/1000\n",
      "10953/10953 [==============================] - 10s 938us/step - loss: 14088.3496 - val_loss: 12106.8687\n",
      "\n",
      "Epoch 00018: val_loss improved from 13083.28488 to 12106.86874, saving model to lstm_model.h5\n",
      "Epoch 19/1000\n",
      "10953/10953 [==============================] - 11s 964us/step - loss: 12877.7379 - val_loss: 10924.0083\n",
      "\n",
      "Epoch 00019: val_loss improved from 12106.86874 to 10924.00835, saving model to lstm_model.h5\n",
      "Epoch 20/1000\n",
      "10953/10953 [==============================] - 11s 969us/step - loss: 11928.4794 - val_loss: 9979.4503\n",
      "\n",
      "Epoch 00020: val_loss improved from 10924.00835 to 9979.45029, saving model to lstm_model.h5\n",
      "Epoch 21/1000\n",
      "10953/10953 [==============================] - 10s 870us/step - loss: 10971.1543 - val_loss: 9118.2090\n",
      "\n",
      "Epoch 00021: val_loss improved from 9979.45029 to 9118.20899, saving model to lstm_model.h5\n",
      "Epoch 22/1000\n",
      "10953/10953 [==============================] - 9s 815us/step - loss: 10172.5921 - val_loss: 8341.4524\n",
      "\n",
      "Epoch 00022: val_loss improved from 9118.20899 to 8341.45235, saving model to lstm_model.h5\n",
      "Epoch 23/1000\n",
      "10953/10953 [==============================] - 11s 972us/step - loss: 9464.9530 - val_loss: 7914.6875\n",
      "\n",
      "Epoch 00023: val_loss improved from 8341.45235 to 7914.68747, saving model to lstm_model.h5\n",
      "Epoch 24/1000\n",
      "10953/10953 [==============================] - 10s 872us/step - loss: 8613.0527 - val_loss: 7036.5890\n",
      "\n",
      "Epoch 00024: val_loss improved from 7914.68747 to 7036.58895, saving model to lstm_model.h5\n",
      "Epoch 25/1000\n",
      "10953/10953 [==============================] - 9s 860us/step - loss: 8193.5072 - val_loss: 6684.8575\n",
      "\n",
      "Epoch 00025: val_loss improved from 7036.58895 to 6684.85751, saving model to lstm_model.h5\n",
      "Epoch 26/1000\n",
      "10953/10953 [==============================] - 10s 873us/step - loss: 7631.3527 - val_loss: 5949.3555\n",
      "\n",
      "Epoch 00026: val_loss improved from 6684.85751 to 5949.35551, saving model to lstm_model.h5\n",
      "Epoch 27/1000\n",
      "10953/10953 [==============================] - 10s 946us/step - loss: 7206.4032 - val_loss: 5479.0359\n",
      "\n",
      "Epoch 00027: val_loss improved from 5949.35551 to 5479.03591, saving model to lstm_model.h5\n",
      "Epoch 28/1000\n",
      "10953/10953 [==============================] - 11s 979us/step - loss: 6731.5440 - val_loss: 5085.1358\n",
      "\n",
      "Epoch 00028: val_loss improved from 5479.03591 to 5085.13579, saving model to lstm_model.h5\n",
      "Epoch 29/1000\n",
      "10953/10953 [==============================] - 11s 962us/step - loss: 6213.4729 - val_loss: 4629.4640\n",
      "\n",
      "Epoch 00029: val_loss improved from 5085.13579 to 4629.46405, saving model to lstm_model.h5\n",
      "Epoch 30/1000\n",
      "10953/10953 [==============================] - 10s 932us/step - loss: 5935.7670 - val_loss: 4273.9799\n",
      "\n",
      "Epoch 00030: val_loss improved from 4629.46405 to 4273.97987, saving model to lstm_model.h5\n",
      "Epoch 31/1000\n",
      "10953/10953 [==============================] - 11s 1ms/step - loss: 5630.2014 - val_loss: 4064.1925\n",
      "\n",
      "Epoch 00031: val_loss improved from 4273.97987 to 4064.19249, saving model to lstm_model.h5\n",
      "Epoch 32/1000\n",
      "10953/10953 [==============================] - 11s 973us/step - loss: 5349.3523 - val_loss: 3650.3634\n",
      "\n",
      "Epoch 00032: val_loss improved from 4064.19249 to 3650.36339, saving model to lstm_model.h5\n",
      "Epoch 33/1000\n",
      "10953/10953 [==============================] - 10s 901us/step - loss: 4848.3149 - val_loss: 3498.7683\n",
      "\n",
      "Epoch 00033: val_loss improved from 3650.36339 to 3498.76832, saving model to lstm_model.h5\n",
      "Epoch 34/1000\n",
      "10953/10953 [==============================] - 11s 992us/step - loss: 4716.8905 - val_loss: 3111.9255\n",
      "\n",
      "Epoch 00034: val_loss improved from 3498.76832 to 3111.92547, saving model to lstm_model.h5\n",
      "Epoch 35/1000\n",
      "10953/10953 [==============================] - 10s 944us/step - loss: 4478.5262 - val_loss: 2908.4734\n",
      "\n",
      "Epoch 00035: val_loss improved from 3111.92547 to 2908.47340, saving model to lstm_model.h5\n",
      "Epoch 36/1000\n",
      "10953/10953 [==============================] - 9s 806us/step - loss: 4185.3835 - val_loss: 2699.7435\n",
      "\n",
      "Epoch 00036: val_loss improved from 2908.47340 to 2699.74347, saving model to lstm_model.h5\n",
      "Epoch 37/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10953/10953 [==============================] - 9s 842us/step - loss: 3910.6077 - val_loss: 2467.0189\n",
      "\n",
      "Epoch 00037: val_loss improved from 2699.74347 to 2467.01891, saving model to lstm_model.h5\n",
      "Epoch 38/1000\n",
      "10953/10953 [==============================] - 10s 913us/step - loss: 3840.0032 - val_loss: 2436.9925\n",
      "\n",
      "Epoch 00038: val_loss improved from 2467.01891 to 2436.99249, saving model to lstm_model.h5\n",
      "Epoch 39/1000\n",
      "10953/10953 [==============================] - 10s 940us/step - loss: 3753.7116 - val_loss: 2449.4261\n",
      "\n",
      "Epoch 00039: val_loss did not improve from 2436.99249\n",
      "Epoch 40/1000\n",
      "10953/10953 [==============================] - 10s 913us/step - loss: 3493.2975 - val_loss: 2009.0201\n",
      "\n",
      "Epoch 00040: val_loss improved from 2436.99249 to 2009.02013, saving model to lstm_model.h5\n",
      "Epoch 41/1000\n",
      "10953/10953 [==============================] - 10s 871us/step - loss: 3362.8783 - val_loss: 1864.3871\n",
      "\n",
      "Epoch 00041: val_loss improved from 2009.02013 to 1864.38712, saving model to lstm_model.h5\n",
      "Epoch 42/1000\n",
      "10953/10953 [==============================] - 9s 791us/step - loss: 3244.2133 - val_loss: 1959.6373\n",
      "\n",
      "Epoch 00042: val_loss did not improve from 1864.38712\n",
      "Epoch 43/1000\n",
      "10953/10953 [==============================] - 9s 845us/step - loss: 3121.9347 - val_loss: 1632.5071\n",
      "\n",
      "Epoch 00043: val_loss improved from 1864.38712 to 1632.50715, saving model to lstm_model.h5\n",
      "Epoch 44/1000\n",
      "10953/10953 [==============================] - 9s 813us/step - loss: 3019.7247 - val_loss: 1552.9859\n",
      "\n",
      "Epoch 00044: val_loss improved from 1632.50715 to 1552.98587, saving model to lstm_model.h5\n",
      "Epoch 45/1000\n",
      "10953/10953 [==============================] - 9s 816us/step - loss: 2936.8042 - val_loss: 1444.9038\n",
      "\n",
      "Epoch 00045: val_loss improved from 1552.98587 to 1444.90378, saving model to lstm_model.h5\n",
      "Epoch 46/1000\n",
      "10953/10953 [==============================] - 10s 896us/step - loss: 2660.1158 - val_loss: 1502.4277\n",
      "\n",
      "Epoch 00046: val_loss did not improve from 1444.90378\n",
      "Epoch 47/1000\n",
      "10953/10953 [==============================] - 10s 902us/step - loss: 2769.2403 - val_loss: 1270.4431\n",
      "\n",
      "Epoch 00047: val_loss improved from 1444.90378 to 1270.44312, saving model to lstm_model.h5\n",
      "Epoch 48/1000\n",
      "10953/10953 [==============================] - 10s 936us/step - loss: 2604.3408 - val_loss: 1199.7728\n",
      "\n",
      "Epoch 00048: val_loss improved from 1270.44312 to 1199.77279, saving model to lstm_model.h5\n",
      "Epoch 49/1000\n",
      "10953/10953 [==============================] - 10s 915us/step - loss: 2533.2176 - val_loss: 1187.8799\n",
      "\n",
      "Epoch 00049: val_loss improved from 1199.77279 to 1187.87986, saving model to lstm_model.h5\n",
      "Epoch 50/1000\n",
      "10953/10953 [==============================] - 11s 974us/step - loss: 2498.5924 - val_loss: 1095.7919\n",
      "\n",
      "Epoch 00050: val_loss improved from 1187.87986 to 1095.79191, saving model to lstm_model.h5\n",
      "Epoch 51/1000\n",
      "10953/10953 [==============================] - 10s 936us/step - loss: 2379.9584 - val_loss: 1016.4886\n",
      "\n",
      "Epoch 00051: val_loss improved from 1095.79191 to 1016.48858, saving model to lstm_model.h5\n",
      "Epoch 52/1000\n",
      "10953/10953 [==============================] - 11s 993us/step - loss: 2305.8632 - val_loss: 990.8734\n",
      "\n",
      "Epoch 00052: val_loss improved from 1016.48858 to 990.87340, saving model to lstm_model.h5\n",
      "Epoch 53/1000\n",
      "10953/10953 [==============================] - 9s 831us/step - loss: 2279.7226 - val_loss: 969.7448\n",
      "\n",
      "Epoch 00053: val_loss improved from 990.87340 to 969.74485, saving model to lstm_model.h5\n",
      "Epoch 54/1000\n",
      "10953/10953 [==============================] - 9s 818us/step - loss: 2200.0159 - val_loss: 948.5175\n",
      "\n",
      "Epoch 00054: val_loss improved from 969.74485 to 948.51753, saving model to lstm_model.h5\n",
      "Epoch 55/1000\n",
      "10953/10953 [==============================] - 9s 810us/step - loss: 2180.5979 - val_loss: 889.1292\n",
      "\n",
      "Epoch 00055: val_loss improved from 948.51753 to 889.12923, saving model to lstm_model.h5\n",
      "Epoch 56/1000\n",
      "10953/10953 [==============================] - 9s 810us/step - loss: 2246.2412 - val_loss: 807.6054\n",
      "\n",
      "Epoch 00056: val_loss improved from 889.12923 to 807.60539, saving model to lstm_model.h5\n",
      "Epoch 57/1000\n",
      "10953/10953 [==============================] - 10s 902us/step - loss: 2033.3212 - val_loss: 788.3376\n",
      "\n",
      "Epoch 00057: val_loss improved from 807.60539 to 788.33765, saving model to lstm_model.h5\n",
      "Epoch 58/1000\n",
      "10953/10953 [==============================] - 9s 831us/step - loss: 1935.4820 - val_loss: 805.1620\n",
      "\n",
      "Epoch 00058: val_loss did not improve from 788.33765\n",
      "Epoch 59/1000\n",
      "10953/10953 [==============================] - 9s 809us/step - loss: 1996.6682 - val_loss: 835.8705\n",
      "\n",
      "Epoch 00059: val_loss did not improve from 788.33765\n",
      "Epoch 60/1000\n",
      "10953/10953 [==============================] - 10s 878us/step - loss: 1933.8588 - val_loss: 665.4729\n",
      "\n",
      "Epoch 00060: val_loss improved from 788.33765 to 665.47286, saving model to lstm_model.h5\n",
      "Epoch 61/1000\n",
      "10953/10953 [==============================] - 11s 961us/step - loss: 1927.7351 - val_loss: 692.5115\n",
      "\n",
      "Epoch 00061: val_loss did not improve from 665.47286\n",
      "Epoch 62/1000\n",
      "10953/10953 [==============================] - 11s 1ms/step - loss: 1810.2634 - val_loss: 632.4576\n",
      "\n",
      "Epoch 00062: val_loss improved from 665.47286 to 632.45757, saving model to lstm_model.h5\n",
      "Epoch 63/1000\n",
      "10953/10953 [==============================] - 10s 898us/step - loss: 1808.9514 - val_loss: 671.1369\n",
      "\n",
      "Epoch 00063: val_loss did not improve from 632.45757\n",
      "Epoch 64/1000\n",
      "10953/10953 [==============================] - 9s 813us/step - loss: 1850.1967 - val_loss: 595.2574\n",
      "\n",
      "Epoch 00064: val_loss improved from 632.45757 to 595.25737, saving model to lstm_model.h5\n",
      "Epoch 65/1000\n",
      "10953/10953 [==============================] - 11s 1ms/step - loss: 1737.7355 - val_loss: 710.5100\n",
      "\n",
      "Epoch 00065: val_loss did not improve from 595.25737\n",
      "Epoch 66/1000\n",
      "10953/10953 [==============================] - 9s 864us/step - loss: 1766.1799 - val_loss: 567.0501\n",
      "\n",
      "Epoch 00066: val_loss improved from 595.25737 to 567.05006, saving model to lstm_model.h5\n",
      "Epoch 67/1000\n",
      "10953/10953 [==============================] - 9s 843us/step - loss: 1732.4535 - val_loss: 527.8446\n",
      "\n",
      "Epoch 00067: val_loss improved from 567.05006 to 527.84459, saving model to lstm_model.h5\n",
      "Epoch 68/1000\n",
      "10953/10953 [==============================] - 11s 1ms/step - loss: 1719.2974 - val_loss: 529.4459\n",
      "\n",
      "Epoch 00068: val_loss did not improve from 527.84459\n",
      "Epoch 69/1000\n",
      "10953/10953 [==============================] - 10s 903us/step - loss: 1749.2044 - val_loss: 514.1901\n",
      "\n",
      "Epoch 00069: val_loss improved from 527.84459 to 514.19005, saving model to lstm_model.h5\n",
      "Epoch 70/1000\n",
      "10953/10953 [==============================] - 9s 853us/step - loss: 1670.2643 - val_loss: 528.4134\n",
      "\n",
      "Epoch 00070: val_loss did not improve from 514.19005\n",
      "Epoch 71/1000\n",
      "10953/10953 [==============================] - 9s 845us/step - loss: 1675.1202 - val_loss: 566.9556\n",
      "\n",
      "Epoch 00071: val_loss did not improve from 514.19005\n",
      "Epoch 72/1000\n",
      "10953/10953 [==============================] - 9s 849us/step - loss: 1710.9201 - val_loss: 517.7023\n",
      "\n",
      "Epoch 00072: val_loss did not improve from 514.19005\n",
      "Epoch 73/1000\n",
      "10953/10953 [==============================] - 9s 839us/step - loss: 1644.1000 - val_loss: 451.0436\n",
      "\n",
      "Epoch 00073: val_loss improved from 514.19005 to 451.04360, saving model to lstm_model.h5\n",
      "Epoch 74/1000\n",
      "10953/10953 [==============================] - 9s 845us/step - loss: 1571.7626 - val_loss: 486.1101\n",
      "\n",
      "Epoch 00074: val_loss did not improve from 451.04360\n",
      "Epoch 75/1000\n",
      "10953/10953 [==============================] - 9s 838us/step - loss: 1498.3040 - val_loss: 490.8060\n",
      "\n",
      "Epoch 00075: val_loss did not improve from 451.04360\n",
      "Epoch 76/1000\n",
      "10953/10953 [==============================] - 9s 837us/step - loss: 1541.6979 - val_loss: 453.9594\n",
      "\n",
      "Epoch 00076: val_loss did not improve from 451.04360\n",
      "Epoch 77/1000\n",
      "10953/10953 [==============================] - 9s 813us/step - loss: 1524.1944 - val_loss: 424.7305\n",
      "\n",
      "Epoch 00077: val_loss improved from 451.04360 to 424.73045, saving model to lstm_model.h5\n",
      "Epoch 78/1000\n",
      "10953/10953 [==============================] - 9s 858us/step - loss: 1519.2301 - val_loss: 411.4527\n",
      "\n",
      "Epoch 00078: val_loss improved from 424.73045 to 411.45268, saving model to lstm_model.h5\n",
      "Epoch 79/1000\n",
      "10953/10953 [==============================] - 9s 845us/step - loss: 1461.3737 - val_loss: 406.9359\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00079: val_loss improved from 411.45268 to 406.93589, saving model to lstm_model.h5\n",
      "Epoch 80/1000\n",
      "10953/10953 [==============================] - 10s 890us/step - loss: 1455.0824 - val_loss: 381.9346\n",
      "\n",
      "Epoch 00080: val_loss improved from 406.93589 to 381.93458, saving model to lstm_model.h5\n",
      "Epoch 81/1000\n",
      "10953/10953 [==============================] - 10s 871us/step - loss: 1525.4564 - val_loss: 468.1891\n",
      "\n",
      "Epoch 00081: val_loss did not improve from 381.93458\n",
      "Epoch 82/1000\n",
      "10953/10953 [==============================] - 9s 807us/step - loss: 1460.6527 - val_loss: 330.1367\n",
      "\n",
      "Epoch 00082: val_loss improved from 381.93458 to 330.13667, saving model to lstm_model.h5\n",
      "Epoch 83/1000\n",
      "10953/10953 [==============================] - 9s 842us/step - loss: 1440.0715 - val_loss: 385.1299\n",
      "\n",
      "Epoch 00083: val_loss did not improve from 330.13667\n",
      "Epoch 84/1000\n",
      "10953/10953 [==============================] - 10s 872us/step - loss: 1526.0320 - val_loss: 351.7352\n",
      "\n",
      "Epoch 00084: val_loss did not improve from 330.13667\n",
      "Epoch 85/1000\n",
      "10953/10953 [==============================] - 9s 847us/step - loss: 1511.5709 - val_loss: 346.4170\n",
      "\n",
      "Epoch 00085: val_loss did not improve from 330.13667\n",
      "Epoch 86/1000\n",
      "10953/10953 [==============================] - 9s 835us/step - loss: 1418.7917 - val_loss: 464.7588\n",
      "\n",
      "Epoch 00086: val_loss did not improve from 330.13667\n",
      "Epoch 87/1000\n",
      "10953/10953 [==============================] - 9s 813us/step - loss: 1422.7559 - val_loss: 362.8461\n",
      "\n",
      "Epoch 00087: val_loss did not improve from 330.13667\n",
      "Epoch 88/1000\n",
      "10953/10953 [==============================] - 9s 827us/step - loss: 1497.2952 - val_loss: 323.1083\n",
      "\n",
      "Epoch 00088: val_loss improved from 330.13667 to 323.10831, saving model to lstm_model.h5\n",
      "Epoch 89/1000\n",
      "10953/10953 [==============================] - 9s 814us/step - loss: 1396.5119 - val_loss: 374.5143\n",
      "\n",
      "Epoch 00089: val_loss did not improve from 323.10831\n",
      "Epoch 90/1000\n",
      "10953/10953 [==============================] - 9s 789us/step - loss: 1360.2333 - val_loss: 318.0367\n",
      "\n",
      "Epoch 00090: val_loss improved from 323.10831 to 318.03670, saving model to lstm_model.h5\n",
      "Epoch 91/1000\n",
      "10953/10953 [==============================] - 9s 798us/step - loss: 1319.5670 - val_loss: 298.1263\n",
      "\n",
      "Epoch 00091: val_loss improved from 318.03670 to 298.12628, saving model to lstm_model.h5\n",
      "Epoch 92/1000\n",
      "10953/10953 [==============================] - 10s 868us/step - loss: 1399.0998 - val_loss: 371.2814\n",
      "\n",
      "Epoch 00092: val_loss did not improve from 298.12628\n",
      "Epoch 93/1000\n",
      "10953/10953 [==============================] - 9s 826us/step - loss: 1389.7499 - val_loss: 280.6439\n",
      "\n",
      "Epoch 00093: val_loss improved from 298.12628 to 280.64389, saving model to lstm_model.h5\n",
      "Epoch 94/1000\n",
      "10953/10953 [==============================] - 9s 859us/step - loss: 1329.7828 - val_loss: 295.6631\n",
      "\n",
      "Epoch 00094: val_loss did not improve from 280.64389\n",
      "Epoch 95/1000\n",
      "10953/10953 [==============================] - 9s 818us/step - loss: 1350.6258 - val_loss: 360.2018\n",
      "\n",
      "Epoch 00095: val_loss did not improve from 280.64389\n",
      "Epoch 96/1000\n",
      "10953/10953 [==============================] - 9s 799us/step - loss: 1287.2770 - val_loss: 281.8708\n",
      "\n",
      "Epoch 00096: val_loss did not improve from 280.64389\n",
      "Epoch 97/1000\n",
      "10953/10953 [==============================] - 9s 801us/step - loss: 1419.1378 - val_loss: 263.7934\n",
      "\n",
      "Epoch 00097: val_loss improved from 280.64389 to 263.79343, saving model to lstm_model.h5\n",
      "Epoch 98/1000\n",
      "10953/10953 [==============================] - 10s 883us/step - loss: 1341.9545 - val_loss: 255.8957\n",
      "\n",
      "Epoch 00098: val_loss improved from 263.79343 to 255.89571, saving model to lstm_model.h5\n",
      "Epoch 99/1000\n",
      "10953/10953 [==============================] - 11s 962us/step - loss: 1348.7856 - val_loss: 245.0659\n",
      "\n",
      "Epoch 00099: val_loss improved from 255.89571 to 245.06594, saving model to lstm_model.h5\n",
      "Epoch 100/1000\n",
      "10953/10953 [==============================] - 9s 807us/step - loss: 1270.3401 - val_loss: 243.6811\n",
      "\n",
      "Epoch 00100: val_loss improved from 245.06594 to 243.68108, saving model to lstm_model.h5\n",
      "Epoch 101/1000\n",
      "10953/10953 [==============================] - 9s 799us/step - loss: 1387.3518 - val_loss: 313.6551\n",
      "\n",
      "Epoch 00101: val_loss did not improve from 243.68108\n",
      "Epoch 102/1000\n",
      "10953/10953 [==============================] - 9s 857us/step - loss: 1264.6211 - val_loss: 295.9096\n",
      "\n",
      "Epoch 00102: val_loss did not improve from 243.68108\n",
      "Epoch 103/1000\n",
      "10953/10953 [==============================] - 9s 817us/step - loss: 1285.2210 - val_loss: 274.3700\n",
      "\n",
      "Epoch 00103: val_loss did not improve from 243.68108\n",
      "Epoch 104/1000\n",
      "10953/10953 [==============================] - 9s 848us/step - loss: 1212.2528 - val_loss: 233.9025\n",
      "\n",
      "Epoch 00104: val_loss improved from 243.68108 to 233.90253, saving model to lstm_model.h5\n",
      "Epoch 105/1000\n",
      "10953/10953 [==============================] - 10s 870us/step - loss: 1211.4702 - val_loss: 262.6097\n",
      "\n",
      "Epoch 00105: val_loss did not improve from 233.90253\n",
      "Epoch 106/1000\n",
      "10953/10953 [==============================] - 9s 843us/step - loss: 1261.0315 - val_loss: 213.3769\n",
      "\n",
      "Epoch 00106: val_loss improved from 233.90253 to 213.37686, saving model to lstm_model.h5\n",
      "Epoch 107/1000\n",
      "10953/10953 [==============================] - 9s 856us/step - loss: 1273.4387 - val_loss: 248.0675\n",
      "\n",
      "Epoch 00107: val_loss did not improve from 213.37686\n",
      "Epoch 108/1000\n",
      "10953/10953 [==============================] - 10s 870us/step - loss: 1271.7415 - val_loss: 198.6294\n",
      "\n",
      "Epoch 00108: val_loss improved from 213.37686 to 198.62937, saving model to lstm_model.h5\n",
      "Epoch 109/1000\n",
      "10953/10953 [==============================] - 9s 817us/step - loss: 1245.9353 - val_loss: 222.1893\n",
      "\n",
      "Epoch 00109: val_loss did not improve from 198.62937\n",
      "Epoch 110/1000\n",
      "10953/10953 [==============================] - 9s 801us/step - loss: 1251.0670 - val_loss: 224.5453\n",
      "\n",
      "Epoch 00110: val_loss did not improve from 198.62937\n",
      "Epoch 111/1000\n",
      "10953/10953 [==============================] - 9s 804us/step - loss: 1268.2291 - val_loss: 225.2660\n",
      "\n",
      "Epoch 00111: val_loss did not improve from 198.62937\n",
      "Epoch 112/1000\n",
      "10953/10953 [==============================] - 9s 805us/step - loss: 1347.1731 - val_loss: 223.5969\n",
      "\n",
      "Epoch 00112: val_loss did not improve from 198.62937\n",
      "Epoch 113/1000\n",
      "10953/10953 [==============================] - 9s 805us/step - loss: 1220.7359 - val_loss: 282.7537\n",
      "\n",
      "Epoch 00113: val_loss did not improve from 198.62937\n",
      "Epoch 114/1000\n",
      "10953/10953 [==============================] - 9s 789us/step - loss: 1146.1441 - val_loss: 244.3200\n",
      "\n",
      "Epoch 00114: val_loss did not improve from 198.62937\n",
      "Epoch 115/1000\n",
      "10953/10953 [==============================] - 9s 800us/step - loss: 1121.5016 - val_loss: 244.2096\n",
      "\n",
      "Epoch 00115: val_loss did not improve from 198.62937\n",
      "Epoch 116/1000\n",
      "10953/10953 [==============================] - 9s 795us/step - loss: 1211.4626 - val_loss: 200.9060\n",
      "\n",
      "Epoch 00116: val_loss did not improve from 198.62937\n",
      "Epoch 117/1000\n",
      "10953/10953 [==============================] - 9s 834us/step - loss: 1279.8337 - val_loss: 293.2071\n",
      "\n",
      "Epoch 00117: val_loss did not improve from 198.62937\n",
      "Epoch 118/1000\n",
      "10953/10953 [==============================] - 9s 851us/step - loss: 1193.5359 - val_loss: 257.2444\n",
      "\n",
      "Epoch 00118: val_loss did not improve from 198.62937\n",
      "Epoch 119/1000\n",
      "10953/10953 [==============================] - 9s 796us/step - loss: 1153.2795 - val_loss: 191.5892\n",
      "\n",
      "Epoch 00119: val_loss improved from 198.62937 to 191.58923, saving model to lstm_model.h5\n",
      "Epoch 120/1000\n",
      "10953/10953 [==============================] - 9s 809us/step - loss: 1176.2257 - val_loss: 275.1128\n",
      "\n",
      "Epoch 00120: val_loss did not improve from 191.58923\n",
      "Epoch 121/1000\n",
      "10953/10953 [==============================] - 9s 805us/step - loss: 1206.7979 - val_loss: 239.4456\n",
      "\n",
      "Epoch 00121: val_loss did not improve from 191.58923\n",
      "Epoch 122/1000\n",
      "10953/10953 [==============================] - 9s 825us/step - loss: 1160.5019 - val_loss: 183.2969\n",
      "\n",
      "Epoch 00122: val_loss improved from 191.58923 to 183.29691, saving model to lstm_model.h5\n",
      "Epoch 123/1000\n",
      "10953/10953 [==============================] - 9s 851us/step - loss: 1208.2200 - val_loss: 258.4848\n",
      "\n",
      "Epoch 00123: val_loss did not improve from 183.29691\n",
      "Epoch 124/1000\n",
      "10953/10953 [==============================] - 9s 833us/step - loss: 1103.1659 - val_loss: 264.9535\n",
      "\n",
      "Epoch 00124: val_loss did not improve from 183.29691\n",
      "Epoch 125/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10953/10953 [==============================] - 9s 854us/step - loss: 1148.2597 - val_loss: 206.9025\n",
      "\n",
      "Epoch 00125: val_loss did not improve from 183.29691\n",
      "Epoch 126/1000\n",
      "10953/10953 [==============================] - 9s 859us/step - loss: 1078.8001 - val_loss: 419.2012\n",
      "\n",
      "Epoch 00126: val_loss did not improve from 183.29691\n",
      "Epoch 127/1000\n",
      "10953/10953 [==============================] - 10s 883us/step - loss: 1161.8429 - val_loss: 225.9298\n",
      "\n",
      "Epoch 00127: val_loss did not improve from 183.29691\n",
      "Epoch 128/1000\n",
      "10953/10953 [==============================] - 10s 887us/step - loss: 1123.2344 - val_loss: 263.2399\n",
      "\n",
      "Epoch 00128: val_loss did not improve from 183.29691\n",
      "Epoch 129/1000\n",
      "10953/10953 [==============================] - 10s 888us/step - loss: 1172.3976 - val_loss: 203.5909\n",
      "\n",
      "Epoch 00129: val_loss did not improve from 183.29691\n",
      "Epoch 130/1000\n",
      "10953/10953 [==============================] - 9s 801us/step - loss: 1204.9028 - val_loss: 204.4173\n",
      "\n",
      "Epoch 00130: val_loss did not improve from 183.29691\n",
      "Epoch 131/1000\n",
      "10953/10953 [==============================] - 9s 800us/step - loss: 1193.8894 - val_loss: 227.6516\n",
      "\n",
      "Epoch 00131: val_loss did not improve from 183.29691\n",
      "Epoch 132/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 1213.1346 - val_loss: 216.6209\n",
      "\n",
      "Epoch 00132: val_loss did not improve from 183.29691\n",
      "Epoch 133/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 1157.0029 - val_loss: 246.6680\n",
      "\n",
      "Epoch 00133: val_loss did not improve from 183.29691\n",
      "Epoch 134/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 1118.4613 - val_loss: 192.6850\n",
      "\n",
      "Epoch 00134: val_loss did not improve from 183.29691\n",
      "Epoch 135/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 1125.6698 - val_loss: 199.1440\n",
      "\n",
      "Epoch 00135: val_loss did not improve from 183.29691\n",
      "Epoch 136/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 1098.1605 - val_loss: 229.3901\n",
      "\n",
      "Epoch 00136: val_loss did not improve from 183.29691\n",
      "Epoch 137/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 1059.6827 - val_loss: 174.0036\n",
      "\n",
      "Epoch 00137: val_loss improved from 183.29691 to 174.00360, saving model to lstm_model.h5\n",
      "Epoch 138/1000\n",
      "10953/10953 [==============================] - 17s 2ms/step - loss: 1095.7027 - val_loss: 205.5806\n",
      "\n",
      "Epoch 00138: val_loss did not improve from 174.00360\n",
      "Epoch 139/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 1069.7010 - val_loss: 182.8497\n",
      "\n",
      "Epoch 00139: val_loss did not improve from 174.00360\n",
      "Epoch 140/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 1108.6384 - val_loss: 310.3543\n",
      "\n",
      "Epoch 00140: val_loss did not improve from 174.00360\n",
      "Epoch 141/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 1149.1293 - val_loss: 181.3862\n",
      "\n",
      "Epoch 00141: val_loss did not improve from 174.00360\n",
      "Epoch 142/1000\n",
      "10953/10953 [==============================] - 17s 2ms/step - loss: 1076.6720 - val_loss: 200.3668\n",
      "\n",
      "Epoch 00142: val_loss did not improve from 174.00360\n",
      "Epoch 143/1000\n",
      "10953/10953 [==============================] - 17s 2ms/step - loss: 1125.8157 - val_loss: 177.9087\n",
      "\n",
      "Epoch 00143: val_loss did not improve from 174.00360\n",
      "Epoch 144/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 1097.7747 - val_loss: 225.7934\n",
      "\n",
      "Epoch 00144: val_loss did not improve from 174.00360\n",
      "Epoch 145/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 1096.2368 - val_loss: 231.3327\n",
      "\n",
      "Epoch 00145: val_loss did not improve from 174.00360\n",
      "Epoch 146/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 1132.3156 - val_loss: 159.3505\n",
      "\n",
      "Epoch 00146: val_loss improved from 174.00360 to 159.35051, saving model to lstm_model.h5\n",
      "Epoch 147/1000\n",
      "10953/10953 [==============================] - 16s 2ms/step - loss: 1104.5456 - val_loss: 227.5995\n",
      "\n",
      "Epoch 00147: val_loss did not improve from 159.35051\n",
      "Epoch 148/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 1027.3019 - val_loss: 219.0423\n",
      "\n",
      "Epoch 00148: val_loss did not improve from 159.35051\n",
      "Epoch 149/1000\n",
      "10953/10953 [==============================] - 17s 2ms/step - loss: 1075.6020 - val_loss: 270.0913\n",
      "\n",
      "Epoch 00149: val_loss did not improve from 159.35051\n",
      "Epoch 150/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 1085.9492 - val_loss: 176.0060\n",
      "\n",
      "Epoch 00150: val_loss did not improve from 159.35051\n",
      "Epoch 151/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 1141.6836 - val_loss: 212.0995\n",
      "\n",
      "Epoch 00151: val_loss did not improve from 159.35051\n",
      "Epoch 152/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 1091.9851 - val_loss: 158.1712\n",
      "\n",
      "Epoch 00152: val_loss improved from 159.35051 to 158.17118, saving model to lstm_model.h5\n",
      "Epoch 153/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 1101.4455 - val_loss: 181.4349\n",
      "\n",
      "Epoch 00153: val_loss did not improve from 158.17118\n",
      "Epoch 154/1000\n",
      "10953/10953 [==============================] - 17s 2ms/step - loss: 1085.3747 - val_loss: 172.4790\n",
      "\n",
      "Epoch 00154: val_loss did not improve from 158.17118\n",
      "Epoch 155/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 1104.6540 - val_loss: 186.5028\n",
      "\n",
      "Epoch 00155: val_loss did not improve from 158.17118\n",
      "Epoch 156/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 1027.9877 - val_loss: 187.2994\n",
      "\n",
      "Epoch 00156: val_loss did not improve from 158.17118\n",
      "Epoch 157/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 1096.7691 - val_loss: 206.1484\n",
      "\n",
      "Epoch 00157: val_loss did not improve from 158.17118\n",
      "Epoch 158/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 1060.1171 - val_loss: 151.2536\n",
      "\n",
      "Epoch 00158: val_loss improved from 158.17118 to 151.25361, saving model to lstm_model.h5\n",
      "Epoch 159/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 1046.4853 - val_loss: 198.9786\n",
      "\n",
      "Epoch 00159: val_loss did not improve from 151.25361\n",
      "Epoch 160/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 1183.1310 - val_loss: 211.1223\n",
      "\n",
      "Epoch 00160: val_loss did not improve from 151.25361\n",
      "Epoch 161/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 1062.6055 - val_loss: 238.3576\n",
      "\n",
      "Epoch 00161: val_loss did not improve from 151.25361\n",
      "Epoch 162/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 1017.3481 - val_loss: 199.5990\n",
      "\n",
      "Epoch 00162: val_loss did not improve from 151.25361\n",
      "Epoch 163/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 1042.9411 - val_loss: 143.7960\n",
      "\n",
      "Epoch 00163: val_loss improved from 151.25361 to 143.79596, saving model to lstm_model.h5\n",
      "Epoch 164/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 1094.8503 - val_loss: 168.9945\n",
      "\n",
      "Epoch 00164: val_loss did not improve from 143.79596\n",
      "Epoch 165/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 1046.8071 - val_loss: 170.5532\n",
      "\n",
      "Epoch 00165: val_loss did not improve from 143.79596\n",
      "Epoch 166/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 1046.0350 - val_loss: 188.5385\n",
      "\n",
      "Epoch 00166: val_loss did not improve from 143.79596\n",
      "Epoch 167/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 1100.2439 - val_loss: 203.2291\n",
      "\n",
      "Epoch 00167: val_loss did not improve from 143.79596\n",
      "Epoch 168/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 1036.2452 - val_loss: 216.6000\n",
      "\n",
      "Epoch 00168: val_loss did not improve from 143.79596\n",
      "Epoch 169/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 1055.2083 - val_loss: 219.9111\n",
      "\n",
      "Epoch 00169: val_loss did not improve from 143.79596\n",
      "Epoch 170/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 1117.7324 - val_loss: 327.2937\n",
      "\n",
      "Epoch 00170: val_loss did not improve from 143.79596\n",
      "Epoch 171/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 1063.2549 - val_loss: 168.4985\n",
      "\n",
      "Epoch 00171: val_loss did not improve from 143.79596\n",
      "Epoch 172/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 1038.1900 - val_loss: 166.4586\n",
      "\n",
      "Epoch 00172: val_loss did not improve from 143.79596\n",
      "Epoch 173/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10953/10953 [==============================] - 16s 1ms/step - loss: 988.8569 - val_loss: 242.0782\n",
      "\n",
      "Epoch 00173: val_loss did not improve from 143.79596\n",
      "Epoch 174/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 1030.6493 - val_loss: 183.3237\n",
      "\n",
      "Epoch 00174: val_loss did not improve from 143.79596\n",
      "Epoch 175/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 1001.3481 - val_loss: 204.1327\n",
      "\n",
      "Epoch 00175: val_loss did not improve from 143.79596\n",
      "Epoch 176/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 1034.8025 - val_loss: 193.4913\n",
      "\n",
      "Epoch 00176: val_loss did not improve from 143.79596\n",
      "Epoch 177/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 1071.2190 - val_loss: 215.6912\n",
      "\n",
      "Epoch 00177: val_loss did not improve from 143.79596\n",
      "Epoch 178/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 1011.2387 - val_loss: 147.0741\n",
      "\n",
      "Epoch 00178: val_loss did not improve from 143.79596\n",
      "Epoch 179/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 1001.6658 - val_loss: 294.6066\n",
      "\n",
      "Epoch 00179: val_loss did not improve from 143.79596\n",
      "Epoch 180/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 974.5372 - val_loss: 210.5826\n",
      "\n",
      "Epoch 00180: val_loss did not improve from 143.79596\n",
      "Epoch 181/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 1022.7205 - val_loss: 205.1591\n",
      "\n",
      "Epoch 00181: val_loss did not improve from 143.79596\n",
      "Epoch 182/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 994.5157 - val_loss: 233.8437\n",
      "\n",
      "Epoch 00182: val_loss did not improve from 143.79596\n",
      "Epoch 183/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 1010.5164 - val_loss: 228.8650\n",
      "\n",
      "Epoch 00183: val_loss did not improve from 143.79596\n",
      "Epoch 184/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 1030.8429 - val_loss: 179.7623\n",
      "\n",
      "Epoch 00184: val_loss did not improve from 143.79596\n",
      "Epoch 185/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 1059.1078 - val_loss: 265.5324\n",
      "\n",
      "Epoch 00185: val_loss did not improve from 143.79596\n",
      "Epoch 186/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 1028.0741 - val_loss: 198.4508\n",
      "\n",
      "Epoch 00186: val_loss did not improve from 143.79596\n",
      "Epoch 187/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 969.0028 - val_loss: 243.6734\n",
      "\n",
      "Epoch 00187: val_loss did not improve from 143.79596\n",
      "Epoch 188/1000\n",
      "10953/10953 [==============================] - 13s 1ms/step - loss: 1012.9879 - val_loss: 311.9481\n",
      "\n",
      "Epoch 00188: val_loss did not improve from 143.79596\n",
      "Epoch 189/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 1030.4594 - val_loss: 190.2069\n",
      "\n",
      "Epoch 00189: val_loss did not improve from 143.79596\n",
      "Epoch 190/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 1029.0763 - val_loss: 186.5653\n",
      "\n",
      "Epoch 00190: val_loss did not improve from 143.79596\n",
      "Epoch 191/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 988.9342 - val_loss: 177.2476\n",
      "\n",
      "Epoch 00191: val_loss did not improve from 143.79596\n",
      "Epoch 192/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 941.5016 - val_loss: 175.4435\n",
      "\n",
      "Epoch 00192: val_loss did not improve from 143.79596\n",
      "Epoch 193/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 1010.9467 - val_loss: 211.8119\n",
      "\n",
      "Epoch 00193: val_loss did not improve from 143.79596\n",
      "Epoch 194/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 963.0805 - val_loss: 212.5490\n",
      "\n",
      "Epoch 00194: val_loss did not improve from 143.79596\n",
      "Epoch 195/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 946.9819 - val_loss: 195.3047\n",
      "\n",
      "Epoch 00195: val_loss did not improve from 143.79596\n",
      "Epoch 196/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 1200.4358 - val_loss: 258.6361\n",
      "\n",
      "Epoch 00196: val_loss did not improve from 143.79596\n",
      "Epoch 197/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 1004.5775 - val_loss: 221.7273\n",
      "\n",
      "Epoch 00197: val_loss did not improve from 143.79596\n",
      "Epoch 198/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 985.6858 - val_loss: 154.9889\n",
      "\n",
      "Epoch 00198: val_loss did not improve from 143.79596\n",
      "Epoch 199/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 986.9780 - val_loss: 230.8686\n",
      "\n",
      "Epoch 00199: val_loss did not improve from 143.79596\n",
      "Epoch 200/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 1050.1462 - val_loss: 188.5282\n",
      "\n",
      "Epoch 00200: val_loss did not improve from 143.79596\n",
      "Epoch 201/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 961.5529 - val_loss: 235.0566\n",
      "\n",
      "Epoch 00201: val_loss did not improve from 143.79596\n",
      "Epoch 202/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 955.8011 - val_loss: 213.8350\n",
      "\n",
      "Epoch 00202: val_loss did not improve from 143.79596\n",
      "Epoch 203/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 975.2513 - val_loss: 181.4826\n",
      "\n",
      "Epoch 00203: val_loss did not improve from 143.79596\n",
      "Epoch 204/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 981.7870 - val_loss: 187.3893\n",
      "\n",
      "Epoch 00204: val_loss did not improve from 143.79596\n",
      "Epoch 205/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 997.0976 - val_loss: 177.8319\n",
      "\n",
      "Epoch 00205: val_loss did not improve from 143.79596\n",
      "Epoch 206/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 1000.1638 - val_loss: 161.6148\n",
      "\n",
      "Epoch 00206: val_loss did not improve from 143.79596\n",
      "Epoch 207/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 1001.9156 - val_loss: 167.9184\n",
      "\n",
      "Epoch 00207: val_loss did not improve from 143.79596\n",
      "Epoch 208/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 979.2102 - val_loss: 176.4591\n",
      "\n",
      "Epoch 00208: val_loss did not improve from 143.79596\n",
      "Epoch 209/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 964.4680 - val_loss: 210.1792\n",
      "\n",
      "Epoch 00209: val_loss did not improve from 143.79596\n",
      "Epoch 210/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 981.6320 - val_loss: 193.5459\n",
      "\n",
      "Epoch 00210: val_loss did not improve from 143.79596\n",
      "Epoch 211/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 934.6052 - val_loss: 176.8179\n",
      "\n",
      "Epoch 00211: val_loss did not improve from 143.79596\n",
      "Epoch 212/1000\n",
      "10953/10953 [==============================] - 17s 2ms/step - loss: 992.0671 - val_loss: 191.8390\n",
      "\n",
      "Epoch 00212: val_loss did not improve from 143.79596\n",
      "Epoch 213/1000\n",
      "10953/10953 [==============================] - 17s 2ms/step - loss: 922.0326 - val_loss: 264.2265\n",
      "\n",
      "Epoch 00213: val_loss did not improve from 143.79596\n",
      "Epoch 214/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 949.1277 - val_loss: 174.1011\n",
      "\n",
      "Epoch 00214: val_loss did not improve from 143.79596\n",
      "Epoch 215/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 951.8720 - val_loss: 196.8977\n",
      "\n",
      "Epoch 00215: val_loss did not improve from 143.79596\n",
      "Epoch 216/1000\n",
      "10953/10953 [==============================] - 16s 2ms/step - loss: 959.7540 - val_loss: 202.6147\n",
      "\n",
      "Epoch 00216: val_loss did not improve from 143.79596\n",
      "Epoch 217/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 887.9521 - val_loss: 181.8003\n",
      "\n",
      "Epoch 00217: val_loss did not improve from 143.79596\n",
      "Epoch 218/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 965.0576 - val_loss: 182.1537\n",
      "\n",
      "Epoch 00218: val_loss did not improve from 143.79596\n",
      "Epoch 219/1000\n",
      "10953/10953 [==============================] - 17s 2ms/step - loss: 948.2608 - val_loss: 241.1652\n",
      "\n",
      "Epoch 00219: val_loss did not improve from 143.79596\n",
      "Epoch 220/1000\n",
      "10953/10953 [==============================] - 17s 2ms/step - loss: 977.4092 - val_loss: 183.6826\n",
      "\n",
      "Epoch 00220: val_loss did not improve from 143.79596\n",
      "Epoch 221/1000\n",
      "10953/10953 [==============================] - 17s 2ms/step - loss: 975.6475 - val_loss: 194.1733\n",
      "\n",
      "Epoch 00221: val_loss did not improve from 143.79596\n",
      "Epoch 222/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10953/10953 [==============================] - 16s 1ms/step - loss: 992.6081 - val_loss: 195.9215\n",
      "\n",
      "Epoch 00222: val_loss did not improve from 143.79596\n",
      "Epoch 223/1000\n",
      "10953/10953 [==============================] - 17s 2ms/step - loss: 895.5666 - val_loss: 156.6809\n",
      "\n",
      "Epoch 00223: val_loss did not improve from 143.79596\n",
      "Epoch 224/1000\n",
      "10953/10953 [==============================] - 17s 2ms/step - loss: 969.9266 - val_loss: 153.4596\n",
      "\n",
      "Epoch 00224: val_loss did not improve from 143.79596\n",
      "Epoch 225/1000\n",
      "10953/10953 [==============================] - 16s 2ms/step - loss: 931.4460 - val_loss: 173.6462\n",
      "\n",
      "Epoch 00225: val_loss did not improve from 143.79596\n",
      "Epoch 226/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 984.3455 - val_loss: 192.5947\n",
      "\n",
      "Epoch 00226: val_loss did not improve from 143.79596\n",
      "Epoch 227/1000\n",
      "10953/10953 [==============================] - 17s 2ms/step - loss: 911.2391 - val_loss: 204.8632\n",
      "\n",
      "Epoch 00227: val_loss did not improve from 143.79596\n",
      "Epoch 228/1000\n",
      "10953/10953 [==============================] - 16s 2ms/step - loss: 966.7886 - val_loss: 160.7276\n",
      "\n",
      "Epoch 00228: val_loss did not improve from 143.79596\n",
      "Epoch 229/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 936.0472 - val_loss: 142.3075\n",
      "\n",
      "Epoch 00229: val_loss improved from 143.79596 to 142.30753, saving model to lstm_model.h5\n",
      "Epoch 230/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 960.3063 - val_loss: 197.1725\n",
      "\n",
      "Epoch 00230: val_loss did not improve from 142.30753\n",
      "Epoch 231/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 941.6570 - val_loss: 183.4339\n",
      "\n",
      "Epoch 00231: val_loss did not improve from 142.30753\n",
      "Epoch 232/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 886.5249 - val_loss: 207.6107\n",
      "\n",
      "Epoch 00232: val_loss did not improve from 142.30753\n",
      "Epoch 233/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 972.1552 - val_loss: 181.4225\n",
      "\n",
      "Epoch 00233: val_loss did not improve from 142.30753\n",
      "Epoch 234/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 952.8149 - val_loss: 174.0556\n",
      "\n",
      "Epoch 00234: val_loss did not improve from 142.30753\n",
      "Epoch 235/1000\n",
      "10953/10953 [==============================] - 17s 2ms/step - loss: 891.7262 - val_loss: 173.8569\n",
      "\n",
      "Epoch 00235: val_loss did not improve from 142.30753\n",
      "Epoch 236/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 916.7390 - val_loss: 214.7058\n",
      "\n",
      "Epoch 00236: val_loss did not improve from 142.30753\n",
      "Epoch 237/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 894.9932 - val_loss: 182.1975\n",
      "\n",
      "Epoch 00237: val_loss did not improve from 142.30753\n",
      "Epoch 238/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 929.6375 - val_loss: 198.1570\n",
      "\n",
      "Epoch 00238: val_loss did not improve from 142.30753\n",
      "Epoch 239/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 907.2999 - val_loss: 237.9304\n",
      "\n",
      "Epoch 00239: val_loss did not improve from 142.30753\n",
      "Epoch 240/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 910.1562 - val_loss: 219.9699\n",
      "\n",
      "Epoch 00240: val_loss did not improve from 142.30753\n",
      "Epoch 241/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 871.9359 - val_loss: 158.2359\n",
      "\n",
      "Epoch 00241: val_loss did not improve from 142.30753\n",
      "Epoch 242/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 886.5256 - val_loss: 175.9053\n",
      "\n",
      "Epoch 00242: val_loss did not improve from 142.30753\n",
      "Epoch 243/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 950.8704 - val_loss: 184.0534\n",
      "\n",
      "Epoch 00243: val_loss did not improve from 142.30753\n",
      "Epoch 244/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 935.4235 - val_loss: 154.0245\n",
      "\n",
      "Epoch 00244: val_loss did not improve from 142.30753\n",
      "Epoch 245/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 902.9895 - val_loss: 174.3422\n",
      "\n",
      "Epoch 00245: val_loss did not improve from 142.30753\n",
      "Epoch 246/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 927.4256 - val_loss: 147.2105\n",
      "\n",
      "Epoch 00246: val_loss did not improve from 142.30753\n",
      "Epoch 247/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 957.2199 - val_loss: 211.3646\n",
      "\n",
      "Epoch 00247: val_loss did not improve from 142.30753\n",
      "Epoch 248/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 934.5872 - val_loss: 167.7708\n",
      "\n",
      "Epoch 00248: val_loss did not improve from 142.30753\n",
      "Epoch 249/1000\n",
      "10953/10953 [==============================] - 17s 2ms/step - loss: 918.2113 - val_loss: 298.3114\n",
      "\n",
      "Epoch 00249: val_loss did not improve from 142.30753\n",
      "Epoch 250/1000\n",
      "10953/10953 [==============================] - 17s 2ms/step - loss: 887.8711 - val_loss: 146.1919\n",
      "\n",
      "Epoch 00250: val_loss did not improve from 142.30753\n",
      "Epoch 251/1000\n",
      "10953/10953 [==============================] - 16s 2ms/step - loss: 925.2640 - val_loss: 150.2909\n",
      "\n",
      "Epoch 00251: val_loss did not improve from 142.30753\n",
      "Epoch 252/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 893.3190 - val_loss: 181.6128\n",
      "\n",
      "Epoch 00252: val_loss did not improve from 142.30753\n",
      "Epoch 253/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 912.5039 - val_loss: 335.4542\n",
      "\n",
      "Epoch 00253: val_loss did not improve from 142.30753\n",
      "Epoch 254/1000\n",
      "10953/10953 [==============================] - 17s 2ms/step - loss: 1014.3050 - val_loss: 203.1016\n",
      "\n",
      "Epoch 00254: val_loss did not improve from 142.30753\n",
      "Epoch 255/1000\n",
      "10953/10953 [==============================] - 17s 2ms/step - loss: 932.3485 - val_loss: 203.5223\n",
      "\n",
      "Epoch 00255: val_loss did not improve from 142.30753\n",
      "Epoch 256/1000\n",
      "10953/10953 [==============================] - 17s 2ms/step - loss: 880.7073 - val_loss: 163.0224\n",
      "\n",
      "Epoch 00256: val_loss did not improve from 142.30753\n",
      "Epoch 257/1000\n",
      "10953/10953 [==============================] - 17s 2ms/step - loss: 909.2791 - val_loss: 182.2472\n",
      "\n",
      "Epoch 00257: val_loss did not improve from 142.30753\n",
      "Epoch 258/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 911.5701 - val_loss: 181.2614\n",
      "\n",
      "Epoch 00258: val_loss did not improve from 142.30753\n",
      "Epoch 259/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 987.6649 - val_loss: 190.7494\n",
      "\n",
      "Epoch 00259: val_loss did not improve from 142.30753\n",
      "Epoch 260/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 948.6098 - val_loss: 226.1731\n",
      "\n",
      "Epoch 00260: val_loss did not improve from 142.30753\n",
      "Epoch 261/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 911.5149 - val_loss: 197.2822\n",
      "\n",
      "Epoch 00261: val_loss did not improve from 142.30753\n",
      "Epoch 262/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 936.5444 - val_loss: 193.7401\n",
      "\n",
      "Epoch 00262: val_loss did not improve from 142.30753\n",
      "Epoch 263/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 892.5678 - val_loss: 200.1667\n",
      "\n",
      "Epoch 00263: val_loss did not improve from 142.30753\n",
      "Epoch 264/1000\n",
      "10953/10953 [==============================] - 17s 2ms/step - loss: 890.4520 - val_loss: 223.5842\n",
      "\n",
      "Epoch 00264: val_loss did not improve from 142.30753\n",
      "Epoch 265/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 906.9757 - val_loss: 158.1623\n",
      "\n",
      "Epoch 00265: val_loss did not improve from 142.30753\n",
      "Epoch 266/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 956.1809 - val_loss: 207.3264\n",
      "\n",
      "Epoch 00266: val_loss did not improve from 142.30753\n",
      "Epoch 267/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 935.0166 - val_loss: 215.0498\n",
      "\n",
      "Epoch 00267: val_loss did not improve from 142.30753\n",
      "Epoch 268/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 869.1805 - val_loss: 181.2104\n",
      "\n",
      "Epoch 00268: val_loss did not improve from 142.30753\n",
      "Epoch 269/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 887.4540 - val_loss: 175.5529\n",
      "\n",
      "Epoch 00269: val_loss did not improve from 142.30753\n",
      "Epoch 270/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 870.8759 - val_loss: 246.3679\n",
      "\n",
      "Epoch 00270: val_loss did not improve from 142.30753\n",
      "Epoch 271/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10953/10953 [==============================] - 15s 1ms/step - loss: 880.6641 - val_loss: 188.8392\n",
      "\n",
      "Epoch 00271: val_loss did not improve from 142.30753\n",
      "Epoch 272/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 898.3958 - val_loss: 173.9007\n",
      "\n",
      "Epoch 00272: val_loss did not improve from 142.30753\n",
      "Epoch 273/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 907.1510 - val_loss: 153.5956\n",
      "\n",
      "Epoch 00273: val_loss did not improve from 142.30753\n",
      "Epoch 274/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 943.5013 - val_loss: 195.6946\n",
      "\n",
      "Epoch 00274: val_loss did not improve from 142.30753\n",
      "Epoch 275/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 888.6750 - val_loss: 193.1444\n",
      "\n",
      "Epoch 00275: val_loss did not improve from 142.30753\n",
      "Epoch 276/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 876.4701 - val_loss: 167.7647\n",
      "\n",
      "Epoch 00276: val_loss did not improve from 142.30753\n",
      "Epoch 277/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 837.9568 - val_loss: 179.8432\n",
      "\n",
      "Epoch 00277: val_loss did not improve from 142.30753\n",
      "Epoch 278/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 1018.4985 - val_loss: 211.9570\n",
      "\n",
      "Epoch 00278: val_loss did not improve from 142.30753\n",
      "Epoch 279/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 883.2858 - val_loss: 183.1581\n",
      "\n",
      "Epoch 00279: val_loss did not improve from 142.30753\n",
      "Epoch 280/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 900.2174 - val_loss: 169.5214\n",
      "\n",
      "Epoch 00280: val_loss did not improve from 142.30753\n",
      "Epoch 281/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 880.9932 - val_loss: 200.5671\n",
      "\n",
      "Epoch 00281: val_loss did not improve from 142.30753\n",
      "Epoch 282/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 852.5877 - val_loss: 183.6615\n",
      "\n",
      "Epoch 00282: val_loss did not improve from 142.30753\n",
      "Epoch 283/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 865.4671 - val_loss: 180.0846\n",
      "\n",
      "Epoch 00283: val_loss did not improve from 142.30753\n",
      "Epoch 284/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 828.1301 - val_loss: 186.2445\n",
      "\n",
      "Epoch 00284: val_loss did not improve from 142.30753\n",
      "Epoch 285/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 889.5055 - val_loss: 171.1346\n",
      "\n",
      "Epoch 00285: val_loss did not improve from 142.30753\n",
      "Epoch 286/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 839.8282 - val_loss: 170.2574\n",
      "\n",
      "Epoch 00286: val_loss did not improve from 142.30753\n",
      "Epoch 287/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 863.1375 - val_loss: 179.4992\n",
      "\n",
      "Epoch 00287: val_loss did not improve from 142.30753\n",
      "Epoch 288/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 862.5120 - val_loss: 201.0026\n",
      "\n",
      "Epoch 00288: val_loss did not improve from 142.30753\n",
      "Epoch 289/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 835.6568 - val_loss: 182.1704\n",
      "\n",
      "Epoch 00289: val_loss did not improve from 142.30753\n",
      "Epoch 290/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 880.7729 - val_loss: 197.2686\n",
      "\n",
      "Epoch 00290: val_loss did not improve from 142.30753\n",
      "Epoch 291/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 865.0814 - val_loss: 209.4324\n",
      "\n",
      "Epoch 00291: val_loss did not improve from 142.30753\n",
      "Epoch 292/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 886.8561 - val_loss: 166.0938\n",
      "\n",
      "Epoch 00292: val_loss did not improve from 142.30753\n",
      "Epoch 293/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 833.8099 - val_loss: 194.8244\n",
      "\n",
      "Epoch 00293: val_loss did not improve from 142.30753\n",
      "Epoch 294/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 894.8832 - val_loss: 172.9817\n",
      "\n",
      "Epoch 00294: val_loss did not improve from 142.30753\n",
      "Epoch 295/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 875.5356 - val_loss: 179.2970\n",
      "\n",
      "Epoch 00295: val_loss did not improve from 142.30753\n",
      "Epoch 296/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 868.4879 - val_loss: 154.1810\n",
      "\n",
      "Epoch 00296: val_loss did not improve from 142.30753\n",
      "Epoch 297/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 867.5704 - val_loss: 158.1425\n",
      "\n",
      "Epoch 00297: val_loss did not improve from 142.30753\n",
      "Epoch 298/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 870.4415 - val_loss: 160.1805\n",
      "\n",
      "Epoch 00298: val_loss did not improve from 142.30753\n",
      "Epoch 299/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 868.4396 - val_loss: 205.9943\n",
      "\n",
      "Epoch 00299: val_loss did not improve from 142.30753\n",
      "Epoch 300/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 889.0846 - val_loss: 303.6139\n",
      "\n",
      "Epoch 00300: val_loss did not improve from 142.30753\n",
      "Epoch 301/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 872.7489 - val_loss: 161.7250\n",
      "\n",
      "Epoch 00301: val_loss did not improve from 142.30753\n",
      "Epoch 302/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 868.3953 - val_loss: 262.2452\n",
      "\n",
      "Epoch 00302: val_loss did not improve from 142.30753\n",
      "Epoch 303/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 878.5420 - val_loss: 190.2524\n",
      "\n",
      "Epoch 00303: val_loss did not improve from 142.30753\n",
      "Epoch 304/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 913.5173 - val_loss: 202.4208\n",
      "\n",
      "Epoch 00304: val_loss did not improve from 142.30753\n",
      "Epoch 305/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 879.4807 - val_loss: 179.4721\n",
      "\n",
      "Epoch 00305: val_loss did not improve from 142.30753\n",
      "Epoch 306/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 870.0756 - val_loss: 270.9669\n",
      "\n",
      "Epoch 00306: val_loss did not improve from 142.30753\n",
      "Epoch 307/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 859.8789 - val_loss: 174.5273\n",
      "\n",
      "Epoch 00307: val_loss did not improve from 142.30753\n",
      "Epoch 308/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 834.9416 - val_loss: 202.1713\n",
      "\n",
      "Epoch 00308: val_loss did not improve from 142.30753\n",
      "Epoch 309/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 825.5889 - val_loss: 172.3564\n",
      "\n",
      "Epoch 00309: val_loss did not improve from 142.30753\n",
      "Epoch 310/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 847.3974 - val_loss: 187.4297\n",
      "\n",
      "Epoch 00310: val_loss did not improve from 142.30753\n",
      "Epoch 311/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 892.8966 - val_loss: 174.5631\n",
      "\n",
      "Epoch 00311: val_loss did not improve from 142.30753\n",
      "Epoch 312/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 831.0811 - val_loss: 182.0761\n",
      "\n",
      "Epoch 00312: val_loss did not improve from 142.30753\n",
      "Epoch 313/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 841.0578 - val_loss: 153.1970\n",
      "\n",
      "Epoch 00313: val_loss did not improve from 142.30753\n",
      "Epoch 314/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 858.2112 - val_loss: 180.6715\n",
      "\n",
      "Epoch 00314: val_loss did not improve from 142.30753\n",
      "Epoch 315/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 872.5593 - val_loss: 214.2102\n",
      "\n",
      "Epoch 00315: val_loss did not improve from 142.30753\n",
      "Epoch 316/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 865.0385 - val_loss: 195.7121\n",
      "\n",
      "Epoch 00316: val_loss did not improve from 142.30753\n",
      "Epoch 317/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 892.7407 - val_loss: 249.3581\n",
      "\n",
      "Epoch 00317: val_loss did not improve from 142.30753\n",
      "Epoch 318/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 864.2316 - val_loss: 145.2330\n",
      "\n",
      "Epoch 00318: val_loss did not improve from 142.30753\n",
      "Epoch 319/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 895.5627 - val_loss: 227.3285\n",
      "\n",
      "Epoch 00319: val_loss did not improve from 142.30753\n",
      "Epoch 320/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 912.9543 - val_loss: 175.2969\n",
      "\n",
      "Epoch 00320: val_loss did not improve from 142.30753\n",
      "Epoch 321/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 875.1160 - val_loss: 182.6695\n",
      "\n",
      "Epoch 00321: val_loss did not improve from 142.30753\n",
      "Epoch 322/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 837.0293 - val_loss: 154.7979\n",
      "\n",
      "Epoch 00322: val_loss did not improve from 142.30753\n",
      "Epoch 323/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 841.0255 - val_loss: 184.2935\n",
      "\n",
      "Epoch 00323: val_loss did not improve from 142.30753\n",
      "Epoch 324/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 898.9242 - val_loss: 224.8111\n",
      "\n",
      "Epoch 00324: val_loss did not improve from 142.30753\n",
      "Epoch 325/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 850.2493 - val_loss: 173.9345\n",
      "\n",
      "Epoch 00325: val_loss did not improve from 142.30753\n",
      "Epoch 326/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 831.7501 - val_loss: 237.7747\n",
      "\n",
      "Epoch 00326: val_loss did not improve from 142.30753\n",
      "Epoch 327/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 850.3209 - val_loss: 181.4776\n",
      "\n",
      "Epoch 00327: val_loss did not improve from 142.30753\n",
      "Epoch 328/1000\n",
      "10953/10953 [==============================] - 106s 10ms/step - loss: 845.1314 - val_loss: 162.6617\n",
      "\n",
      "Epoch 00328: val_loss did not improve from 142.30753\n",
      "Epoch 329/1000\n",
      "10953/10953 [==============================] - 18s 2ms/step - loss: 837.2243 - val_loss: 179.1495\n",
      "\n",
      "Epoch 00329: val_loss did not improve from 142.30753\n",
      "Epoch 330/1000\n",
      "10953/10953 [==============================] - 18s 2ms/step - loss: 855.7537 - val_loss: 186.6721\n",
      "\n",
      "Epoch 00330: val_loss did not improve from 142.30753\n",
      "Epoch 331/1000\n",
      "10953/10953 [==============================] - 17s 2ms/step - loss: 809.0631 - val_loss: 201.8014\n",
      "\n",
      "Epoch 00331: val_loss did not improve from 142.30753\n",
      "Epoch 332/1000\n",
      "10953/10953 [==============================] - 17s 2ms/step - loss: 816.0316 - val_loss: 142.3258\n",
      "\n",
      "Epoch 00332: val_loss did not improve from 142.30753\n",
      "Epoch 333/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 832.9717 - val_loss: 198.4622\n",
      "\n",
      "Epoch 00333: val_loss did not improve from 142.30753\n",
      "Epoch 334/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 835.1226 - val_loss: 185.2131\n",
      "\n",
      "Epoch 00334: val_loss did not improve from 142.30753\n",
      "Epoch 335/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 839.5230 - val_loss: 164.1199\n",
      "\n",
      "Epoch 00335: val_loss did not improve from 142.30753\n",
      "Epoch 336/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 830.9923 - val_loss: 151.4769\n",
      "\n",
      "Epoch 00336: val_loss did not improve from 142.30753\n",
      "Epoch 337/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 801.7202 - val_loss: 239.7723\n",
      "\n",
      "Epoch 00337: val_loss did not improve from 142.30753\n",
      "Epoch 338/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 862.1256 - val_loss: 190.4649\n",
      "\n",
      "Epoch 00338: val_loss did not improve from 142.30753\n",
      "Epoch 339/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 797.1744 - val_loss: 156.0596\n",
      "\n",
      "Epoch 00339: val_loss did not improve from 142.30753\n",
      "Epoch 340/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 818.0388 - val_loss: 187.3214\n",
      "\n",
      "Epoch 00340: val_loss did not improve from 142.30753\n",
      "Epoch 341/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 859.6513 - val_loss: 182.5968\n",
      "\n",
      "Epoch 00341: val_loss did not improve from 142.30753\n",
      "Epoch 342/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 790.6412 - val_loss: 264.5440\n",
      "\n",
      "Epoch 00342: val_loss did not improve from 142.30753\n",
      "Epoch 343/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 886.3148 - val_loss: 185.1061\n",
      "\n",
      "Epoch 00343: val_loss did not improve from 142.30753\n",
      "Epoch 344/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 846.0090 - val_loss: 167.6388\n",
      "\n",
      "Epoch 00344: val_loss did not improve from 142.30753\n",
      "Epoch 345/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 827.8246 - val_loss: 173.3668\n",
      "\n",
      "Epoch 00345: val_loss did not improve from 142.30753\n",
      "Epoch 346/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 884.3467 - val_loss: 171.4222\n",
      "\n",
      "Epoch 00346: val_loss did not improve from 142.30753\n",
      "Epoch 347/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 795.2115 - val_loss: 177.4639\n",
      "\n",
      "Epoch 00347: val_loss did not improve from 142.30753\n",
      "Epoch 348/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 845.9129 - val_loss: 157.5165\n",
      "\n",
      "Epoch 00348: val_loss did not improve from 142.30753\n",
      "Epoch 349/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 788.6133 - val_loss: 220.4804\n",
      "\n",
      "Epoch 00349: val_loss did not improve from 142.30753\n",
      "Epoch 350/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 822.3842 - val_loss: 150.4936\n",
      "\n",
      "Epoch 00350: val_loss did not improve from 142.30753\n",
      "Epoch 351/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 835.8033 - val_loss: 198.1883\n",
      "\n",
      "Epoch 00351: val_loss did not improve from 142.30753\n",
      "Epoch 352/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 827.8296 - val_loss: 139.1008\n",
      "\n",
      "Epoch 00352: val_loss improved from 142.30753 to 139.10081, saving model to lstm_model.h5\n",
      "Epoch 353/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 843.4630 - val_loss: 144.7561\n",
      "\n",
      "Epoch 00353: val_loss did not improve from 139.10081\n",
      "Epoch 354/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 813.9112 - val_loss: 183.3724\n",
      "\n",
      "Epoch 00354: val_loss did not improve from 139.10081\n",
      "Epoch 355/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 767.5787 - val_loss: 189.3935\n",
      "\n",
      "Epoch 00355: val_loss did not improve from 139.10081\n",
      "Epoch 356/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 856.8623 - val_loss: 177.6032\n",
      "\n",
      "Epoch 00356: val_loss did not improve from 139.10081\n",
      "Epoch 357/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 794.7724 - val_loss: 222.1333\n",
      "\n",
      "Epoch 00357: val_loss did not improve from 139.10081\n",
      "Epoch 358/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 807.7484 - val_loss: 123.9042\n",
      "\n",
      "Epoch 00358: val_loss improved from 139.10081 to 123.90418, saving model to lstm_model.h5\n",
      "Epoch 359/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 769.5672 - val_loss: 162.5301\n",
      "\n",
      "Epoch 00359: val_loss did not improve from 123.90418\n",
      "Epoch 360/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 818.7923 - val_loss: 221.0420\n",
      "\n",
      "Epoch 00360: val_loss did not improve from 123.90418\n",
      "Epoch 361/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 834.9094 - val_loss: 152.7881\n",
      "\n",
      "Epoch 00361: val_loss did not improve from 123.90418\n",
      "Epoch 362/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 842.7291 - val_loss: 169.4791\n",
      "\n",
      "Epoch 00362: val_loss did not improve from 123.90418\n",
      "Epoch 363/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 791.0382 - val_loss: 185.8231\n",
      "\n",
      "Epoch 00363: val_loss did not improve from 123.90418\n",
      "Epoch 364/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 800.4670 - val_loss: 184.0423\n",
      "\n",
      "Epoch 00364: val_loss did not improve from 123.90418\n",
      "Epoch 365/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 828.0252 - val_loss: 166.9715\n",
      "\n",
      "Epoch 00365: val_loss did not improve from 123.90418\n",
      "Epoch 366/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 784.0763 - val_loss: 157.1629\n",
      "\n",
      "Epoch 00366: val_loss did not improve from 123.90418\n",
      "Epoch 367/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 811.9488 - val_loss: 189.9153\n",
      "\n",
      "Epoch 00367: val_loss did not improve from 123.90418\n",
      "Epoch 368/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 827.9196 - val_loss: 176.0539\n",
      "\n",
      "Epoch 00368: val_loss did not improve from 123.90418\n",
      "Epoch 369/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10953/10953 [==============================] - 14s 1ms/step - loss: 792.8950 - val_loss: 163.2768\n",
      "\n",
      "Epoch 00369: val_loss did not improve from 123.90418\n",
      "Epoch 370/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 810.1102 - val_loss: 176.5696\n",
      "\n",
      "Epoch 00370: val_loss did not improve from 123.90418\n",
      "Epoch 371/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 809.5602 - val_loss: 168.1454\n",
      "\n",
      "Epoch 00371: val_loss did not improve from 123.90418\n",
      "Epoch 372/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 906.8891 - val_loss: 433.5648\n",
      "\n",
      "Epoch 00372: val_loss did not improve from 123.90418\n",
      "Epoch 373/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 797.9089 - val_loss: 171.5467\n",
      "\n",
      "Epoch 00373: val_loss did not improve from 123.90418\n",
      "Epoch 374/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 802.9780 - val_loss: 199.0533\n",
      "\n",
      "Epoch 00374: val_loss did not improve from 123.90418\n",
      "Epoch 375/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 743.0376 - val_loss: 213.4980\n",
      "\n",
      "Epoch 00375: val_loss did not improve from 123.90418\n",
      "Epoch 376/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 817.7736 - val_loss: 177.6469\n",
      "\n",
      "Epoch 00376: val_loss did not improve from 123.90418\n",
      "Epoch 377/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 782.0008 - val_loss: 155.7308\n",
      "\n",
      "Epoch 00377: val_loss did not improve from 123.90418\n",
      "Epoch 378/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 810.4449 - val_loss: 152.6933\n",
      "\n",
      "Epoch 00378: val_loss did not improve from 123.90418\n",
      "Epoch 379/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 825.6613 - val_loss: 165.8036\n",
      "\n",
      "Epoch 00379: val_loss did not improve from 123.90418\n",
      "Epoch 380/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 816.3612 - val_loss: 209.8429\n",
      "\n",
      "Epoch 00380: val_loss did not improve from 123.90418\n",
      "Epoch 381/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 792.1983 - val_loss: 147.4782\n",
      "\n",
      "Epoch 00381: val_loss did not improve from 123.90418\n",
      "Epoch 382/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 829.9144 - val_loss: 213.5773\n",
      "\n",
      "Epoch 00382: val_loss did not improve from 123.90418\n",
      "Epoch 383/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 786.5742 - val_loss: 147.0494\n",
      "\n",
      "Epoch 00383: val_loss did not improve from 123.90418\n",
      "Epoch 384/1000\n",
      "10953/10953 [==============================] - 17s 2ms/step - loss: 817.4042 - val_loss: 222.8391\n",
      "\n",
      "Epoch 00384: val_loss did not improve from 123.90418\n",
      "Epoch 385/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 801.4587 - val_loss: 241.3086\n",
      "\n",
      "Epoch 00385: val_loss did not improve from 123.90418\n",
      "Epoch 386/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 813.7829 - val_loss: 145.2861\n",
      "\n",
      "Epoch 00386: val_loss did not improve from 123.90418\n",
      "Epoch 387/1000\n",
      "10953/10953 [==============================] - 17s 2ms/step - loss: 804.8786 - val_loss: 170.2074\n",
      "\n",
      "Epoch 00387: val_loss did not improve from 123.90418\n",
      "Epoch 388/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 753.3823 - val_loss: 171.3738\n",
      "\n",
      "Epoch 00388: val_loss did not improve from 123.90418\n",
      "Epoch 389/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 804.3411 - val_loss: 238.3114\n",
      "\n",
      "Epoch 00389: val_loss did not improve from 123.90418\n",
      "Epoch 390/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 859.3287 - val_loss: 192.4055\n",
      "\n",
      "Epoch 00390: val_loss did not improve from 123.90418\n",
      "Epoch 391/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 750.2020 - val_loss: 189.9010\n",
      "\n",
      "Epoch 00391: val_loss did not improve from 123.90418\n",
      "Epoch 392/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 802.9416 - val_loss: 198.1335\n",
      "\n",
      "Epoch 00392: val_loss did not improve from 123.90418\n",
      "Epoch 393/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 788.6174 - val_loss: 153.0761\n",
      "\n",
      "Epoch 00393: val_loss did not improve from 123.90418\n",
      "Epoch 394/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 779.5258 - val_loss: 177.1965\n",
      "\n",
      "Epoch 00394: val_loss did not improve from 123.90418\n",
      "Epoch 395/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 788.1883 - val_loss: 196.6664\n",
      "\n",
      "Epoch 00395: val_loss did not improve from 123.90418\n",
      "Epoch 396/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 824.3778 - val_loss: 151.2391\n",
      "\n",
      "Epoch 00396: val_loss did not improve from 123.90418\n",
      "Epoch 397/1000\n",
      "10953/10953 [==============================] - 18s 2ms/step - loss: 791.7823 - val_loss: 201.1234\n",
      "\n",
      "Epoch 00397: val_loss did not improve from 123.90418\n",
      "Epoch 398/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 832.1482 - val_loss: 182.7280\n",
      "\n",
      "Epoch 00398: val_loss did not improve from 123.90418\n",
      "Epoch 399/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 827.0895 - val_loss: 209.7208\n",
      "\n",
      "Epoch 00399: val_loss did not improve from 123.90418\n",
      "Epoch 400/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 843.7815 - val_loss: 180.5805\n",
      "\n",
      "Epoch 00400: val_loss did not improve from 123.90418\n",
      "Epoch 401/1000\n",
      "10953/10953 [==============================] - 17s 2ms/step - loss: 755.2613 - val_loss: 172.7582\n",
      "\n",
      "Epoch 00401: val_loss did not improve from 123.90418\n",
      "Epoch 402/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 773.5300 - val_loss: 165.3181\n",
      "\n",
      "Epoch 00402: val_loss did not improve from 123.90418\n",
      "Epoch 403/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 790.2747 - val_loss: 199.6243\n",
      "\n",
      "Epoch 00403: val_loss did not improve from 123.90418\n",
      "Epoch 404/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 782.6650 - val_loss: 169.5351\n",
      "\n",
      "Epoch 00404: val_loss did not improve from 123.90418\n",
      "Epoch 405/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 777.5478 - val_loss: 156.9926\n",
      "\n",
      "Epoch 00405: val_loss did not improve from 123.90418\n",
      "Epoch 406/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 790.8127 - val_loss: 189.4694\n",
      "\n",
      "Epoch 00406: val_loss did not improve from 123.90418\n",
      "Epoch 407/1000\n",
      "10953/10953 [==============================] - 17s 2ms/step - loss: 791.8411 - val_loss: 140.1799\n",
      "\n",
      "Epoch 00407: val_loss did not improve from 123.90418\n",
      "Epoch 408/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 823.5118 - val_loss: 133.9627\n",
      "\n",
      "Epoch 00408: val_loss did not improve from 123.90418\n",
      "Epoch 409/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 779.2953 - val_loss: 151.9749\n",
      "\n",
      "Epoch 00409: val_loss did not improve from 123.90418\n",
      "Epoch 410/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 748.4587 - val_loss: 177.0789\n",
      "\n",
      "Epoch 00410: val_loss did not improve from 123.90418\n",
      "Epoch 411/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 755.5834 - val_loss: 168.9779\n",
      "\n",
      "Epoch 00411: val_loss did not improve from 123.90418\n",
      "Epoch 412/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 784.7789 - val_loss: 148.9454\n",
      "\n",
      "Epoch 00412: val_loss did not improve from 123.90418\n",
      "Epoch 413/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 774.5413 - val_loss: 174.3332\n",
      "\n",
      "Epoch 00413: val_loss did not improve from 123.90418\n",
      "Epoch 414/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 738.9939 - val_loss: 149.8213\n",
      "\n",
      "Epoch 00414: val_loss did not improve from 123.90418\n",
      "Epoch 415/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 772.9418 - val_loss: 163.2924\n",
      "\n",
      "Epoch 00415: val_loss did not improve from 123.90418\n",
      "Epoch 416/1000\n",
      "10953/10953 [==============================] - 17s 2ms/step - loss: 769.8779 - val_loss: 188.6078\n",
      "\n",
      "Epoch 00416: val_loss did not improve from 123.90418\n",
      "Epoch 417/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 771.4537 - val_loss: 157.1206\n",
      "\n",
      "Epoch 00417: val_loss did not improve from 123.90418\n",
      "Epoch 418/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 829.1905 - val_loss: 205.5481\n",
      "\n",
      "Epoch 00418: val_loss did not improve from 123.90418\n",
      "Epoch 419/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 797.9317 - val_loss: 185.4332\n",
      "\n",
      "Epoch 00419: val_loss did not improve from 123.90418\n",
      "Epoch 420/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 805.1733 - val_loss: 151.1795\n",
      "\n",
      "Epoch 00420: val_loss did not improve from 123.90418\n",
      "Epoch 421/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 796.7712 - val_loss: 173.2862\n",
      "\n",
      "Epoch 00421: val_loss did not improve from 123.90418\n",
      "Epoch 422/1000\n",
      "10953/10953 [==============================] - 17s 2ms/step - loss: 795.7135 - val_loss: 169.5411\n",
      "\n",
      "Epoch 00422: val_loss did not improve from 123.90418\n",
      "Epoch 423/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 764.9727 - val_loss: 165.8598\n",
      "\n",
      "Epoch 00423: val_loss did not improve from 123.90418\n",
      "Epoch 424/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 721.8949 - val_loss: 201.1206\n",
      "\n",
      "Epoch 00424: val_loss did not improve from 123.90418\n",
      "Epoch 425/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 765.7168 - val_loss: 148.4775\n",
      "\n",
      "Epoch 00425: val_loss did not improve from 123.90418\n",
      "Epoch 426/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 766.8048 - val_loss: 193.1058\n",
      "\n",
      "Epoch 00426: val_loss did not improve from 123.90418\n",
      "Epoch 427/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 727.7504 - val_loss: 191.4311\n",
      "\n",
      "Epoch 00427: val_loss did not improve from 123.90418\n",
      "Epoch 428/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 796.8781 - val_loss: 204.6250\n",
      "\n",
      "Epoch 00428: val_loss did not improve from 123.90418\n",
      "Epoch 429/1000\n",
      "10953/10953 [==============================] - 17s 2ms/step - loss: 751.7829 - val_loss: 180.1193\n",
      "\n",
      "Epoch 00429: val_loss did not improve from 123.90418\n",
      "Epoch 430/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 772.2988 - val_loss: 165.9845\n",
      "\n",
      "Epoch 00430: val_loss did not improve from 123.90418\n",
      "Epoch 431/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 753.5502 - val_loss: 161.2199\n",
      "\n",
      "Epoch 00431: val_loss did not improve from 123.90418\n",
      "Epoch 432/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 767.4049 - val_loss: 187.2545\n",
      "\n",
      "Epoch 00432: val_loss did not improve from 123.90418\n",
      "Epoch 433/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 756.4381 - val_loss: 187.9965\n",
      "\n",
      "Epoch 00433: val_loss did not improve from 123.90418\n",
      "Epoch 434/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 787.1976 - val_loss: 145.8783\n",
      "\n",
      "Epoch 00434: val_loss did not improve from 123.90418\n",
      "Epoch 435/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 781.1053 - val_loss: 144.4894\n",
      "\n",
      "Epoch 00435: val_loss did not improve from 123.90418\n",
      "Epoch 436/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 862.8980 - val_loss: 543.1702\n",
      "\n",
      "Epoch 00436: val_loss did not improve from 123.90418\n",
      "Epoch 437/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 816.1486 - val_loss: 257.3319\n",
      "\n",
      "Epoch 00437: val_loss did not improve from 123.90418\n",
      "Epoch 438/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 789.7849 - val_loss: 152.7265\n",
      "\n",
      "Epoch 00438: val_loss did not improve from 123.90418\n",
      "Epoch 439/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 805.2197 - val_loss: 160.4863\n",
      "\n",
      "Epoch 00439: val_loss did not improve from 123.90418\n",
      "Epoch 440/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 741.4847 - val_loss: 142.7815\n",
      "\n",
      "Epoch 00440: val_loss did not improve from 123.90418\n",
      "Epoch 441/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 747.1578 - val_loss: 180.0763\n",
      "\n",
      "Epoch 00441: val_loss did not improve from 123.90418\n",
      "Epoch 442/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 777.6789 - val_loss: 159.8985\n",
      "\n",
      "Epoch 00442: val_loss did not improve from 123.90418\n",
      "Epoch 443/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 770.7977 - val_loss: 171.8490\n",
      "\n",
      "Epoch 00443: val_loss did not improve from 123.90418\n",
      "Epoch 444/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 765.7235 - val_loss: 173.6585\n",
      "\n",
      "Epoch 00444: val_loss did not improve from 123.90418\n",
      "Epoch 445/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 785.2594 - val_loss: 168.4972\n",
      "\n",
      "Epoch 00445: val_loss did not improve from 123.90418\n",
      "Epoch 446/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 765.7281 - val_loss: 185.6114\n",
      "\n",
      "Epoch 00446: val_loss did not improve from 123.90418\n",
      "Epoch 447/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 773.8354 - val_loss: 220.5780\n",
      "\n",
      "Epoch 00447: val_loss did not improve from 123.90418\n",
      "Epoch 448/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 786.2616 - val_loss: 177.2887\n",
      "\n",
      "Epoch 00448: val_loss did not improve from 123.90418\n",
      "Epoch 449/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 751.2963 - val_loss: 201.8852\n",
      "\n",
      "Epoch 00449: val_loss did not improve from 123.90418\n",
      "Epoch 450/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 725.0291 - val_loss: 176.3761\n",
      "\n",
      "Epoch 00450: val_loss did not improve from 123.90418\n",
      "Epoch 451/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 800.0604 - val_loss: 158.0931\n",
      "\n",
      "Epoch 00451: val_loss did not improve from 123.90418\n",
      "Epoch 452/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 768.3656 - val_loss: 187.8841\n",
      "\n",
      "Epoch 00452: val_loss did not improve from 123.90418\n",
      "Epoch 453/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 765.6464 - val_loss: 163.6074\n",
      "\n",
      "Epoch 00453: val_loss did not improve from 123.90418\n",
      "Epoch 454/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 793.8370 - val_loss: 183.9348\n",
      "\n",
      "Epoch 00454: val_loss did not improve from 123.90418\n",
      "Epoch 455/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 757.0335 - val_loss: 203.7518\n",
      "\n",
      "Epoch 00455: val_loss did not improve from 123.90418\n",
      "Epoch 456/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 762.3579 - val_loss: 159.9059\n",
      "\n",
      "Epoch 00456: val_loss did not improve from 123.90418\n",
      "Epoch 457/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 759.2091 - val_loss: 188.7359\n",
      "\n",
      "Epoch 00457: val_loss did not improve from 123.90418\n",
      "Epoch 458/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 711.8431 - val_loss: 202.9316\n",
      "\n",
      "Epoch 00458: val_loss did not improve from 123.90418\n",
      "Epoch 459/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 779.7652 - val_loss: 171.8734\n",
      "\n",
      "Epoch 00459: val_loss did not improve from 123.90418\n",
      "Epoch 460/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 736.5496 - val_loss: 220.6266\n",
      "\n",
      "Epoch 00460: val_loss did not improve from 123.90418\n",
      "Epoch 461/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 752.9396 - val_loss: 165.5932\n",
      "\n",
      "Epoch 00461: val_loss did not improve from 123.90418\n",
      "Epoch 462/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 737.3638 - val_loss: 179.1009\n",
      "\n",
      "Epoch 00462: val_loss did not improve from 123.90418\n",
      "Epoch 463/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 765.8609 - val_loss: 184.4360\n",
      "\n",
      "Epoch 00463: val_loss did not improve from 123.90418\n",
      "Epoch 464/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 717.4446 - val_loss: 261.0070\n",
      "\n",
      "Epoch 00464: val_loss did not improve from 123.90418\n",
      "Epoch 465/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 737.5886 - val_loss: 150.8973\n",
      "\n",
      "Epoch 00465: val_loss did not improve from 123.90418\n",
      "Epoch 466/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 732.1773 - val_loss: 193.0798\n",
      "\n",
      "Epoch 00466: val_loss did not improve from 123.90418\n",
      "Epoch 467/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10953/10953 [==============================] - 15s 1ms/step - loss: 723.9460 - val_loss: 175.5850\n",
      "\n",
      "Epoch 00467: val_loss did not improve from 123.90418\n",
      "Epoch 468/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 737.0161 - val_loss: 179.7022\n",
      "\n",
      "Epoch 00468: val_loss did not improve from 123.90418\n",
      "Epoch 469/1000\n",
      "10953/10953 [==============================] - 17s 2ms/step - loss: 759.4770 - val_loss: 158.9169\n",
      "\n",
      "Epoch 00469: val_loss did not improve from 123.90418\n",
      "Epoch 470/1000\n",
      "10953/10953 [==============================] - 17s 2ms/step - loss: 758.4205 - val_loss: 204.5722\n",
      "\n",
      "Epoch 00470: val_loss did not improve from 123.90418\n",
      "Epoch 471/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 735.9091 - val_loss: 196.1962\n",
      "\n",
      "Epoch 00471: val_loss did not improve from 123.90418\n",
      "Epoch 472/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 737.7928 - val_loss: 186.3386\n",
      "\n",
      "Epoch 00472: val_loss did not improve from 123.90418\n",
      "Epoch 473/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 741.2713 - val_loss: 171.3083\n",
      "\n",
      "Epoch 00473: val_loss did not improve from 123.90418\n",
      "Epoch 474/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 734.3833 - val_loss: 197.5006\n",
      "\n",
      "Epoch 00474: val_loss did not improve from 123.90418\n",
      "Epoch 475/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 730.6694 - val_loss: 246.7028\n",
      "\n",
      "Epoch 00475: val_loss did not improve from 123.90418\n",
      "Epoch 476/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 766.8748 - val_loss: 405.0983\n",
      "\n",
      "Epoch 00476: val_loss did not improve from 123.90418\n",
      "Epoch 477/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 812.9905 - val_loss: 284.6304\n",
      "\n",
      "Epoch 00477: val_loss did not improve from 123.90418\n",
      "Epoch 478/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 682.4058 - val_loss: 239.5670\n",
      "\n",
      "Epoch 00478: val_loss did not improve from 123.90418\n",
      "Epoch 479/1000\n",
      "10953/10953 [==============================] - 17s 2ms/step - loss: 744.1276 - val_loss: 189.2588\n",
      "\n",
      "Epoch 00479: val_loss did not improve from 123.90418\n",
      "Epoch 480/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 735.2342 - val_loss: 160.4232\n",
      "\n",
      "Epoch 00480: val_loss did not improve from 123.90418\n",
      "Epoch 481/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 724.5399 - val_loss: 172.9496\n",
      "\n",
      "Epoch 00481: val_loss did not improve from 123.90418\n",
      "Epoch 482/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 714.8226 - val_loss: 165.1670\n",
      "\n",
      "Epoch 00482: val_loss did not improve from 123.90418\n",
      "Epoch 483/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 751.6051 - val_loss: 204.9121\n",
      "\n",
      "Epoch 00483: val_loss did not improve from 123.90418\n",
      "Epoch 484/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 781.8460 - val_loss: 200.0598\n",
      "\n",
      "Epoch 00484: val_loss did not improve from 123.90418\n",
      "Epoch 485/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 739.1719 - val_loss: 272.4644\n",
      "\n",
      "Epoch 00485: val_loss did not improve from 123.90418\n",
      "Epoch 486/1000\n",
      "10953/10953 [==============================] - 16s 2ms/step - loss: 752.7989 - val_loss: 175.7646\n",
      "\n",
      "Epoch 00486: val_loss did not improve from 123.90418\n",
      "Epoch 487/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 722.4916 - val_loss: 169.2540\n",
      "\n",
      "Epoch 00487: val_loss did not improve from 123.90418\n",
      "Epoch 488/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 734.4252 - val_loss: 176.3988\n",
      "\n",
      "Epoch 00488: val_loss did not improve from 123.90418\n",
      "Epoch 489/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 738.6399 - val_loss: 172.8586\n",
      "\n",
      "Epoch 00489: val_loss did not improve from 123.90418\n",
      "Epoch 490/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 779.4656 - val_loss: 189.0431\n",
      "\n",
      "Epoch 00490: val_loss did not improve from 123.90418\n",
      "Epoch 491/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 727.0348 - val_loss: 179.3620\n",
      "\n",
      "Epoch 00491: val_loss did not improve from 123.90418\n",
      "Epoch 492/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 734.0028 - val_loss: 183.6747\n",
      "\n",
      "Epoch 00492: val_loss did not improve from 123.90418\n",
      "Epoch 493/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 723.3606 - val_loss: 184.1355\n",
      "\n",
      "Epoch 00493: val_loss did not improve from 123.90418\n",
      "Epoch 494/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 744.1808 - val_loss: 198.1278\n",
      "\n",
      "Epoch 00494: val_loss did not improve from 123.90418\n",
      "Epoch 495/1000\n",
      "10953/10953 [==============================] - 18s 2ms/step - loss: 799.2444 - val_loss: 140.9034\n",
      "\n",
      "Epoch 00495: val_loss did not improve from 123.90418\n",
      "Epoch 496/1000\n",
      "10953/10953 [==============================] - 16s 2ms/step - loss: 704.1025 - val_loss: 137.3934\n",
      "\n",
      "Epoch 00496: val_loss did not improve from 123.90418\n",
      "Epoch 497/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 733.0705 - val_loss: 173.6014\n",
      "\n",
      "Epoch 00497: val_loss did not improve from 123.90418\n",
      "Epoch 498/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 712.8854 - val_loss: 233.1821\n",
      "\n",
      "Epoch 00498: val_loss did not improve from 123.90418\n",
      "Epoch 499/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 768.1177 - val_loss: 216.0285\n",
      "\n",
      "Epoch 00499: val_loss did not improve from 123.90418\n",
      "Epoch 500/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 724.4468 - val_loss: 193.0572\n",
      "\n",
      "Epoch 00500: val_loss did not improve from 123.90418\n",
      "Epoch 501/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 737.3573 - val_loss: 185.8266\n",
      "\n",
      "Epoch 00501: val_loss did not improve from 123.90418\n",
      "Epoch 502/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 740.9270 - val_loss: 204.1344\n",
      "\n",
      "Epoch 00502: val_loss did not improve from 123.90418\n",
      "Epoch 503/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 695.4839 - val_loss: 200.1071\n",
      "\n",
      "Epoch 00503: val_loss did not improve from 123.90418\n",
      "Epoch 504/1000\n",
      "10953/10953 [==============================] - 17s 2ms/step - loss: 720.6496 - val_loss: 192.4143\n",
      "\n",
      "Epoch 00504: val_loss did not improve from 123.90418\n",
      "Epoch 505/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 712.5674 - val_loss: 160.9320\n",
      "\n",
      "Epoch 00505: val_loss did not improve from 123.90418\n",
      "Epoch 506/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 727.2287 - val_loss: 204.4105\n",
      "\n",
      "Epoch 00506: val_loss did not improve from 123.90418\n",
      "Epoch 507/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 697.5462 - val_loss: 186.1862\n",
      "\n",
      "Epoch 00507: val_loss did not improve from 123.90418\n",
      "Epoch 508/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 735.2477 - val_loss: 244.0231\n",
      "\n",
      "Epoch 00508: val_loss did not improve from 123.90418\n",
      "Epoch 509/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 702.7718 - val_loss: 194.3653\n",
      "\n",
      "Epoch 00509: val_loss did not improve from 123.90418\n",
      "Epoch 510/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 848.3654 - val_loss: 448.3738\n",
      "\n",
      "Epoch 00510: val_loss did not improve from 123.90418\n",
      "Epoch 511/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 760.8666 - val_loss: 177.4958\n",
      "\n",
      "Epoch 00511: val_loss did not improve from 123.90418\n",
      "Epoch 512/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 711.1053 - val_loss: 203.5207\n",
      "\n",
      "Epoch 00512: val_loss did not improve from 123.90418\n",
      "Epoch 513/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 721.7847 - val_loss: 189.6586\n",
      "\n",
      "Epoch 00513: val_loss did not improve from 123.90418\n",
      "Epoch 514/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 706.4637 - val_loss: 158.5442\n",
      "\n",
      "Epoch 00514: val_loss did not improve from 123.90418\n",
      "Epoch 515/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 721.8952 - val_loss: 172.0604\n",
      "\n",
      "Epoch 00515: val_loss did not improve from 123.90418\n",
      "Epoch 516/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 723.7211 - val_loss: 183.1682\n",
      "\n",
      "Epoch 00516: val_loss did not improve from 123.90418\n",
      "Epoch 517/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 729.7814 - val_loss: 199.5088\n",
      "\n",
      "Epoch 00517: val_loss did not improve from 123.90418\n",
      "Epoch 518/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 699.0379 - val_loss: 225.4380\n",
      "\n",
      "Epoch 00518: val_loss did not improve from 123.90418\n",
      "Epoch 519/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 749.9645 - val_loss: 203.4267\n",
      "\n",
      "Epoch 00519: val_loss did not improve from 123.90418\n",
      "Epoch 520/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 732.9276 - val_loss: 219.8234\n",
      "\n",
      "Epoch 00520: val_loss did not improve from 123.90418\n",
      "Epoch 521/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 746.1989 - val_loss: 192.5744\n",
      "\n",
      "Epoch 00521: val_loss did not improve from 123.90418\n",
      "Epoch 522/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 731.6515 - val_loss: 256.2502\n",
      "\n",
      "Epoch 00522: val_loss did not improve from 123.90418\n",
      "Epoch 523/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 740.3716 - val_loss: 143.0725\n",
      "\n",
      "Epoch 00523: val_loss did not improve from 123.90418\n",
      "Epoch 524/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 742.4219 - val_loss: 176.2535\n",
      "\n",
      "Epoch 00524: val_loss did not improve from 123.90418\n",
      "Epoch 525/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 715.3509 - val_loss: 242.7787\n",
      "\n",
      "Epoch 00525: val_loss did not improve from 123.90418\n",
      "Epoch 526/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 742.9476 - val_loss: 212.8397\n",
      "\n",
      "Epoch 00526: val_loss did not improve from 123.90418\n",
      "Epoch 527/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 757.4771 - val_loss: 213.3200\n",
      "\n",
      "Epoch 00527: val_loss did not improve from 123.90418\n",
      "Epoch 528/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 738.1225 - val_loss: 566.3446\n",
      "\n",
      "Epoch 00528: val_loss did not improve from 123.90418\n",
      "Epoch 529/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 734.7535 - val_loss: 204.1755\n",
      "\n",
      "Epoch 00529: val_loss did not improve from 123.90418\n",
      "Epoch 530/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 750.1112 - val_loss: 208.8954\n",
      "\n",
      "Epoch 00530: val_loss did not improve from 123.90418\n",
      "Epoch 531/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 713.3444 - val_loss: 208.3802\n",
      "\n",
      "Epoch 00531: val_loss did not improve from 123.90418\n",
      "Epoch 532/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 752.1898 - val_loss: 183.7545\n",
      "\n",
      "Epoch 00532: val_loss did not improve from 123.90418\n",
      "Epoch 533/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 700.9135 - val_loss: 171.2675\n",
      "\n",
      "Epoch 00533: val_loss did not improve from 123.90418\n",
      "Epoch 534/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 699.2763 - val_loss: 235.4073\n",
      "\n",
      "Epoch 00534: val_loss did not improve from 123.90418\n",
      "Epoch 535/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 713.3879 - val_loss: 241.7966\n",
      "\n",
      "Epoch 00535: val_loss did not improve from 123.90418\n",
      "Epoch 536/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 730.4254 - val_loss: 194.9417\n",
      "\n",
      "Epoch 00536: val_loss did not improve from 123.90418\n",
      "Epoch 537/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 713.3014 - val_loss: 209.4048\n",
      "\n",
      "Epoch 00537: val_loss did not improve from 123.90418\n",
      "Epoch 538/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 706.2906 - val_loss: 225.2755\n",
      "\n",
      "Epoch 00538: val_loss did not improve from 123.90418\n",
      "Epoch 539/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 723.6454 - val_loss: 172.4904\n",
      "\n",
      "Epoch 00539: val_loss did not improve from 123.90418\n",
      "Epoch 540/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 730.1085 - val_loss: 165.8193\n",
      "\n",
      "Epoch 00540: val_loss did not improve from 123.90418\n",
      "Epoch 541/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 706.7264 - val_loss: 172.4125\n",
      "\n",
      "Epoch 00541: val_loss did not improve from 123.90418\n",
      "Epoch 542/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 712.3727 - val_loss: 184.4381\n",
      "\n",
      "Epoch 00542: val_loss did not improve from 123.90418\n",
      "Epoch 543/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 725.2014 - val_loss: 215.8890\n",
      "\n",
      "Epoch 00543: val_loss did not improve from 123.90418\n",
      "Epoch 544/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 710.6642 - val_loss: 167.3716\n",
      "\n",
      "Epoch 00544: val_loss did not improve from 123.90418\n",
      "Epoch 545/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 720.4848 - val_loss: 190.9582\n",
      "\n",
      "Epoch 00545: val_loss did not improve from 123.90418\n",
      "Epoch 546/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 685.8327 - val_loss: 182.1287\n",
      "\n",
      "Epoch 00546: val_loss did not improve from 123.90418\n",
      "Epoch 547/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 663.3856 - val_loss: 159.5167\n",
      "\n",
      "Epoch 00547: val_loss did not improve from 123.90418\n",
      "Epoch 548/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 703.3019 - val_loss: 154.0771\n",
      "\n",
      "Epoch 00548: val_loss did not improve from 123.90418\n",
      "Epoch 549/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 771.5713 - val_loss: 201.0092\n",
      "\n",
      "Epoch 00549: val_loss did not improve from 123.90418\n",
      "Epoch 550/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 733.1468 - val_loss: 174.1594\n",
      "\n",
      "Epoch 00550: val_loss did not improve from 123.90418\n",
      "Epoch 551/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 726.7959 - val_loss: 197.3407\n",
      "\n",
      "Epoch 00551: val_loss did not improve from 123.90418\n",
      "Epoch 552/1000\n",
      "10953/10953 [==============================] - 17s 2ms/step - loss: 732.9401 - val_loss: 218.7683\n",
      "\n",
      "Epoch 00552: val_loss did not improve from 123.90418\n",
      "Epoch 553/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 722.5494 - val_loss: 170.3587\n",
      "\n",
      "Epoch 00553: val_loss did not improve from 123.90418\n",
      "Epoch 554/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 741.7086 - val_loss: 287.5635\n",
      "\n",
      "Epoch 00554: val_loss did not improve from 123.90418\n",
      "Epoch 555/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 716.7728 - val_loss: 185.4399\n",
      "\n",
      "Epoch 00555: val_loss did not improve from 123.90418\n",
      "Epoch 556/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 738.5456 - val_loss: 183.7530\n",
      "\n",
      "Epoch 00556: val_loss did not improve from 123.90418\n",
      "Epoch 557/1000\n",
      "10953/10953 [==============================] - 17s 2ms/step - loss: 691.4397 - val_loss: 152.4106\n",
      "\n",
      "Epoch 00557: val_loss did not improve from 123.90418\n",
      "Epoch 558/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 734.5984 - val_loss: 200.6475\n",
      "\n",
      "Epoch 00558: val_loss did not improve from 123.90418\n",
      "Epoch 00558: early stopping\n"
     ]
    }
   ],
   "source": [
    "model_path = 'lstm_model.h5'\n",
    "\n",
    "history = model.fit(to_3D(x_train_lstm, features), y_train, \n",
    "                    epochs=1000, batch_size= 8, validation_split=0.2, verbose=1, \n",
    "                    callbacks = [\n",
    "                            keras.callbacks.EarlyStopping(monitor='val_loss',\n",
    "                                                          min_delta=0, \n",
    "                                                          patience=200,\n",
    "                                                          verbose=1,\n",
    "                                                          mode='min'),\n",
    "\n",
    "                            keras.callbacks.ModelCheckpoint(model_path, \n",
    "                                                            monitor='val_loss',\n",
    "                                                            save_best_only=True, \n",
    "                                                            mode='min',\n",
    "                                                            verbose=1)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluating Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-19T03:26:35.280667Z",
     "start_time": "2019-09-19T03:26:34.940570Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x19563048908>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8), dpi=90)\n",
    "plt.plot(model.history.history['val_loss'],label='val_loss')\n",
    "plt.plot(model.history.history['loss'],label='loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('MSE')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:18:14.102793Z",
     "start_time": "2019-10-27T15:18:13.961900Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From c:\\users\\danie\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model.load_weights(\"lstm_model.h5\")\n",
    "model.compile(loss='mean_squared_error', optimizer='adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:18:15.162686Z",
     "start_time": "2019-10-27T15:18:14.618143Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score: 0.9926\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "rul_pred = model.predict(to_3D(x_test_lstm,features))\n",
    "print(f\"R2 Score: {round(r2_score(y_test, rul_pred),4)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:18:15.871831Z",
     "start_time": "2019-10-27T15:18:15.628439Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x21a50d4a3c8>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,8), dpi=90)\n",
    "plt.plot(y_test.iloc[100:200].values,label = 'Actual Rul')\n",
    "plt.plot(rul_pred[100:200],label='Pred Rul')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:18:16.427303Z",
     "start_time": "2019-10-27T15:18:16.424341Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Converting the regression problem on classification again by setting a failure treshold(RUL Value) less or equal than 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:18:17.654024Z",
     "start_time": "2019-10-27T15:18:17.638066Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.996     0.902     0.947      4474\n",
      "           1      0.143     0.802     0.243        91\n",
      "\n",
      "    accuracy                          0.900      4565\n",
      "   macro avg      0.569     0.852     0.595      4565\n",
      "weighted avg      0.979     0.900     0.933      4565\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print()\n",
    "y_test_class = df.y.reindex(y_test.index)\n",
    "print(classification_report(y_test_class,rul_pred<=5,digits=3))\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:18:20.901318Z",
     "start_time": "2019-10-27T15:18:18.433975Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n\\n\")\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.title(\"Confusion Matrix\", fontdict={\"size\":16}, pad=10)\n",
    "sns.heatmap(confusion_matrix(y_test_class,rul_pred<=5), annot=True, fmt='d', annot_kws={\"size\":16})\n",
    "plt.show()\n",
    "print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It does not have a benchmark performance when converting it back to classification problem but it can be considered in a ensemble."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-19T03:38:24.416314Z",
     "start_time": "2019-09-19T03:38:24.407337Z"
    }
   },
   "source": [
    "## F1 Score for different Failure Thresholds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:18:21.469823Z",
     "start_time": "2019-10-27T15:18:21.465809Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:18:22.037335Z",
     "start_time": "2019-10-27T15:18:22.033305Z"
    }
   },
   "outputs": [],
   "source": [
    "fail_thresh = np.arange(1,31)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:18:22.595787Z",
     "start_time": "2019-10-27T15:18:22.533951Z"
    }
   },
   "outputs": [],
   "source": [
    "f1_scores = np.array([])\n",
    "\n",
    "for ft in fail_thresh:\n",
    "    f1_scores = np.append(f1_scores, round(f1_score(y_test_class, rul_pred<=ft),4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:18:23.130358Z",
     "start_time": "2019-10-27T15:18:23.123376Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.2478, 0.2487, 0.2504, 0.2458, 0.2429, 0.2382, 0.2325, 0.2257,\n",
       "       0.2222, 0.2195, 0.2153, 0.2139, 0.2117, 0.2119, 0.2104, 0.2069,\n",
       "       0.2033, 0.2029, 0.2013, 0.1997, 0.1995, 0.198 , 0.1955, 0.196 ,\n",
       "       0.1961, 0.1935, 0.1912, 0.1893, 0.1874, 0.1852])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:18:23.736809Z",
     "start_time": "2019-10-27T15:18:23.732745Z"
    }
   },
   "outputs": [],
   "source": [
    "max_arg = np.argmax(f1_scores)\n",
    "max_f1 = f1_scores[max_arg]\n",
    "max_thresh = fail_thresh[max_arg]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:18:25.128042Z",
     "start_time": "2019-10-27T15:18:24.935530Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8), dpi=90)\n",
    "plt.title(\"Failure Threshold Analysis\")\n",
    "plt.xlabel(\"Failure Threshold\")\n",
    "plt.ylabel(\"F1-Score\")\n",
    "plt.plot(fail_thresh, f1_scores, lw=2, marker='o')\n",
    "plt.annotate('Max F1', xy=(max_thresh, max_f1),\n",
    "            arrowprops=dict(facecolor='black', shrink=0.05), xytext=(max_thresh+3, max_f1+0.01))\n",
    "plt.ylim(0.18, 0.275)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see in this plot the optimum value for the failure threshold is 3, obtaining the following result:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:18:27.122753Z",
     "start_time": "2019-10-27T15:18:27.099742Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.996     0.906     0.949      4474\n",
      "           1      0.148     0.802     0.250        91\n",
      "\n",
      "    accuracy                          0.904      4565\n",
      "   macro avg      0.572     0.854     0.600      4565\n",
      "weighted avg      0.979     0.904     0.935      4565\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print()\n",
    "y_test_class = df.y.reindex(y_test.index)\n",
    "print(classification_report(y_test_class,rul_pred<=3,digits=3))\n",
    "confusion_matrix(y_test_class,rul_pred<=3)\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:18:28.067154Z",
     "start_time": "2019-10-27T15:18:27.934509Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n\\n\")\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.title(\"Confusion Matrix\", fontdict={\"size\":16}, pad=10)\n",
    "sns.heatmap(confusion_matrix(y_test_class,rul_pred<=3), annot=True, fmt='d', annot_kws={\"size\":16})\n",
    "plt.show()\n",
    "print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the regression problem we sucessful implemented, a classification problem can also been address by LSTM using Classes Weight\n",
    "to compensate the imbalance problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:18:30.641066Z",
     "start_time": "2019-10-27T15:18:30.332927Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From c:\\users\\danie\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow\\python\\ops\\nn_impl.py:180: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_1 (LSTM)                (None, 5, 15)             4620      \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 5, 15)             0         \n",
      "_________________________________________________________________\n",
      "lstm_2 (LSTM)                (None, 10)                1040      \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 10)                0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 11        \n",
      "=================================================================\n",
      "Total params: 5,671\n",
      "Trainable params: 5,671\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "model_class = Sequential()\n",
    "model_class.add(LSTM(input_shape=(timesteps,len(features)), units=15, return_sequences=True))\n",
    "model_class.add(Dropout(0.2))\n",
    "model_class.add(LSTM(input_shape=(timesteps,len(features)), units=10, return_sequences=False))\n",
    "model_class.add(Dropout(0.2))\n",
    "model_class.add(Dense(units=1, activation = 'sigmoid'))\n",
    "model_class.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-09T23:38:48.426473Z",
     "start_time": "2019-10-09T22:40:38.711101Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 10953 samples, validate on 2739 samples\n",
      "Epoch 1/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 1.1947 - acc: 0.7476 - val_loss: 1.1370 - val_acc: 0.7211\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 1.13696, saving model to lstm_model_classification.h5\n",
      "Epoch 2/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.9982 - acc: 0.7655 - val_loss: 1.0329 - val_acc: 0.7660\n",
      "\n",
      "Epoch 00002: val_loss improved from 1.13696 to 1.03285, saving model to lstm_model_classification.h5\n",
      "Epoch 3/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.8437 - acc: 0.7949 - val_loss: 0.9445 - val_acc: 0.8335\n",
      "\n",
      "Epoch 00003: val_loss improved from 1.03285 to 0.94445, saving model to lstm_model_classification.h5\n",
      "Epoch 4/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.7310 - acc: 0.8270 - val_loss: 0.8781 - val_acc: 0.8372\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.94445 to 0.87815, saving model to lstm_model_classification.h5\n",
      "Epoch 5/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.6396 - acc: 0.8433 - val_loss: 0.8457 - val_acc: 0.8591\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.87815 to 0.84568, saving model to lstm_model_classification.h5\n",
      "Epoch 6/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.5452 - acc: 0.8612 - val_loss: 1.0469 - val_acc: 0.8974\n",
      "\n",
      "Epoch 00006: val_loss did not improve from 0.84568\n",
      "Epoch 7/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.4822 - acc: 0.8801 - val_loss: 0.6864 - val_acc: 0.8879\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.84568 to 0.68642, saving model to lstm_model_classification.h5\n",
      "Epoch 8/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.4129 - acc: 0.8977 - val_loss: 0.7526 - val_acc: 0.9091\n",
      "\n",
      "Epoch 00008: val_loss did not improve from 0.68642\n",
      "Epoch 9/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.4049 - acc: 0.8961 - val_loss: 0.7212 - val_acc: 0.8737\n",
      "\n",
      "Epoch 00009: val_loss did not improve from 0.68642\n",
      "Epoch 10/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.3559 - acc: 0.9022 - val_loss: 0.7147 - val_acc: 0.9054\n",
      "\n",
      "Epoch 00010: val_loss did not improve from 0.68642\n",
      "Epoch 11/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.3121 - acc: 0.9229 - val_loss: 0.7443 - val_acc: 0.9259\n",
      "\n",
      "Epoch 00011: val_loss did not improve from 0.68642\n",
      "Epoch 12/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.2955 - acc: 0.9235 - val_loss: 0.7508 - val_acc: 0.9277\n",
      "\n",
      "Epoch 00012: val_loss did not improve from 0.68642\n",
      "Epoch 13/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.2656 - acc: 0.9365 - val_loss: 0.7034 - val_acc: 0.9339\n",
      "\n",
      "Epoch 00013: val_loss did not improve from 0.68642\n",
      "Epoch 14/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.2313 - acc: 0.9433 - val_loss: 0.9744 - val_acc: 0.9460\n",
      "\n",
      "Epoch 00014: val_loss did not improve from 0.68642\n",
      "Epoch 15/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.2213 - acc: 0.9418 - val_loss: 0.8233 - val_acc: 0.9493\n",
      "\n",
      "Epoch 00015: val_loss did not improve from 0.68642\n",
      "Epoch 16/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.1759 - acc: 0.9561 - val_loss: 1.0326 - val_acc: 0.9679\n",
      "\n",
      "Epoch 00016: val_loss did not improve from 0.68642\n",
      "Epoch 17/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.2033 - acc: 0.9543 - val_loss: 0.6137 - val_acc: 0.9288\n",
      "\n",
      "Epoch 00017: val_loss improved from 0.68642 to 0.61368, saving model to lstm_model_classification.h5\n",
      "Epoch 18/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.2069 - acc: 0.9415 - val_loss: 0.9690 - val_acc: 0.9576\n",
      "\n",
      "Epoch 00018: val_loss did not improve from 0.61368\n",
      "Epoch 19/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.2028 - acc: 0.9511 - val_loss: 0.9972 - val_acc: 0.9547\n",
      "\n",
      "Epoch 00019: val_loss did not improve from 0.61368\n",
      "Epoch 20/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.2192 - acc: 0.9492 - val_loss: 0.6222 - val_acc: 0.9416\n",
      "\n",
      "Epoch 00020: val_loss did not improve from 0.61368\n",
      "Epoch 21/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.1708 - acc: 0.9565 - val_loss: 0.9221 - val_acc: 0.9529\n",
      "\n",
      "Epoch 00021: val_loss did not improve from 0.61368\n",
      "Epoch 22/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.1512 - acc: 0.9649 - val_loss: 1.1106 - val_acc: 0.9671\n",
      "\n",
      "Epoch 00022: val_loss did not improve from 0.61368\n",
      "Epoch 23/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.1692 - acc: 0.9665 - val_loss: 0.6831 - val_acc: 0.9576\n",
      "\n",
      "Epoch 00023: val_loss did not improve from 0.61368\n",
      "Epoch 24/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.1266 - acc: 0.9677 - val_loss: 0.9120 - val_acc: 0.9620\n",
      "\n",
      "Epoch 00024: val_loss did not improve from 0.61368\n",
      "Epoch 25/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.1242 - acc: 0.9688 - val_loss: 1.2356 - val_acc: 0.9598\n",
      "\n",
      "Epoch 00025: val_loss did not improve from 0.61368\n",
      "Epoch 26/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.1563 - acc: 0.9645 - val_loss: 0.7624 - val_acc: 0.9485\n",
      "\n",
      "Epoch 00026: val_loss did not improve from 0.61368\n",
      "Epoch 27/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.1580 - acc: 0.9622 - val_loss: 0.8537 - val_acc: 0.9671\n",
      "\n",
      "Epoch 00027: val_loss did not improve from 0.61368\n",
      "Epoch 28/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.1185 - acc: 0.9698 - val_loss: 1.1841 - val_acc: 0.9741\n",
      "\n",
      "Epoch 00028: val_loss did not improve from 0.61368\n",
      "Epoch 29/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.1159 - acc: 0.9719 - val_loss: 1.7029 - val_acc: 0.9682\n",
      "\n",
      "Epoch 00029: val_loss did not improve from 0.61368\n",
      "Epoch 30/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.1202 - acc: 0.9737 - val_loss: 1.3663 - val_acc: 0.9671\n",
      "\n",
      "Epoch 00030: val_loss did not improve from 0.61368\n",
      "Epoch 31/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.1320 - acc: 0.9690 - val_loss: 1.0756 - val_acc: 0.9635\n",
      "\n",
      "Epoch 00031: val_loss did not improve from 0.61368\n",
      "Epoch 32/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.1434 - acc: 0.9741 - val_loss: 0.8304 - val_acc: 0.9690\n",
      "\n",
      "Epoch 00032: val_loss did not improve from 0.61368\n",
      "Epoch 33/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0893 - acc: 0.9791 - val_loss: 0.6162 - val_acc: 0.9733\n",
      "\n",
      "Epoch 00033: val_loss did not improve from 0.61368\n",
      "Epoch 34/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0830 - acc: 0.9799 - val_loss: 1.2020 - val_acc: 0.9774\n",
      "\n",
      "Epoch 00034: val_loss did not improve from 0.61368\n",
      "Epoch 35/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.1160 - acc: 0.9841 - val_loss: 1.4232 - val_acc: 0.9792\n",
      "\n",
      "Epoch 00035: val_loss did not improve from 0.61368\n",
      "Epoch 36/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0929 - acc: 0.9794 - val_loss: 1.0008 - val_acc: 0.9774\n",
      "\n",
      "Epoch 00036: val_loss did not improve from 0.61368\n",
      "Epoch 37/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0603 - acc: 0.9859 - val_loss: 1.3511 - val_acc: 0.9799\n",
      "\n",
      "Epoch 00037: val_loss did not improve from 0.61368\n",
      "Epoch 38/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.1226 - acc: 0.9754 - val_loss: 1.1797 - val_acc: 0.9675\n",
      "\n",
      "Epoch 00038: val_loss did not improve from 0.61368\n",
      "Epoch 39/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0941 - acc: 0.9767 - val_loss: 0.9046 - val_acc: 0.9752\n",
      "\n",
      "Epoch 00039: val_loss did not improve from 0.61368\n",
      "Epoch 40/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0832 - acc: 0.9790 - val_loss: 0.9606 - val_acc: 0.9788\n",
      "\n",
      "Epoch 00040: val_loss did not improve from 0.61368\n",
      "Epoch 41/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0789 - acc: 0.9820 - val_loss: 1.1978 - val_acc: 0.9788\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00041: val_loss did not improve from 0.61368\n",
      "Epoch 42/1000\n",
      "10953/10953 [==============================] - 13s 1ms/step - loss: 0.0647 - acc: 0.9846 - val_loss: 1.5258 - val_acc: 0.9785\n",
      "\n",
      "Epoch 00042: val_loss did not improve from 0.61368\n",
      "Epoch 43/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.1096 - acc: 0.9800 - val_loss: 0.8570 - val_acc: 0.9803\n",
      "\n",
      "Epoch 00043: val_loss did not improve from 0.61368\n",
      "Epoch 44/1000\n",
      "10953/10953 [==============================] - 13s 1ms/step - loss: 0.0708 - acc: 0.9840 - val_loss: 1.1285 - val_acc: 0.9832\n",
      "\n",
      "Epoch 00044: val_loss did not improve from 0.61368\n",
      "Epoch 45/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0861 - acc: 0.9858 - val_loss: 0.7841 - val_acc: 0.9763\n",
      "\n",
      "Epoch 00045: val_loss did not improve from 0.61368\n",
      "Epoch 46/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0815 - acc: 0.9814 - val_loss: 0.7163 - val_acc: 0.9785\n",
      "\n",
      "Epoch 00046: val_loss did not improve from 0.61368\n",
      "Epoch 47/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0878 - acc: 0.9811 - val_loss: 0.5984 - val_acc: 0.9810\n",
      "\n",
      "Epoch 00047: val_loss improved from 0.61368 to 0.59836, saving model to lstm_model_classification.h5\n",
      "Epoch 48/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0813 - acc: 0.9827 - val_loss: 0.7140 - val_acc: 0.9755\n",
      "\n",
      "Epoch 00048: val_loss did not improve from 0.59836\n",
      "Epoch 49/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0750 - acc: 0.9838 - val_loss: 0.9987 - val_acc: 0.9821\n",
      "\n",
      "Epoch 00049: val_loss did not improve from 0.59836\n",
      "Epoch 50/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0681 - acc: 0.9880 - val_loss: 0.9180 - val_acc: 0.9777\n",
      "\n",
      "Epoch 00050: val_loss did not improve from 0.59836\n",
      "Epoch 51/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.1191 - acc: 0.9848 - val_loss: 0.8791 - val_acc: 0.9817\n",
      "\n",
      "Epoch 00051: val_loss did not improve from 0.59836\n",
      "Epoch 52/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.1725 - acc: 0.9807 - val_loss: 0.4437 - val_acc: 0.9744\n",
      "\n",
      "Epoch 00052: val_loss improved from 0.59836 to 0.44367, saving model to lstm_model_classification.h5\n",
      "Epoch 53/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0848 - acc: 0.9804 - val_loss: 0.5728 - val_acc: 0.9821\n",
      "\n",
      "Epoch 00053: val_loss did not improve from 0.44367\n",
      "Epoch 54/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 0.0363 - acc: 0.9917 - val_loss: 1.0588 - val_acc: 0.9865\n",
      "\n",
      "Epoch 00054: val_loss did not improve from 0.44367\n",
      "Epoch 55/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0725 - acc: 0.9878 - val_loss: 0.7292 - val_acc: 0.9744\n",
      "\n",
      "Epoch 00055: val_loss did not improve from 0.44367\n",
      "Epoch 56/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0989 - acc: 0.9855 - val_loss: 0.8152 - val_acc: 0.9759\n",
      "\n",
      "Epoch 00056: val_loss did not improve from 0.44367\n",
      "Epoch 57/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.1067 - acc: 0.9794 - val_loss: 0.9882 - val_acc: 0.9803\n",
      "\n",
      "Epoch 00057: val_loss did not improve from 0.44367\n",
      "Epoch 58/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0680 - acc: 0.9862 - val_loss: 0.7239 - val_acc: 0.9755\n",
      "\n",
      "Epoch 00058: val_loss did not improve from 0.44367\n",
      "Epoch 59/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0528 - acc: 0.9859 - val_loss: 1.2465 - val_acc: 0.9850\n",
      "\n",
      "Epoch 00059: val_loss did not improve from 0.44367\n",
      "Epoch 60/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0775 - acc: 0.9846 - val_loss: 1.3519 - val_acc: 0.9803\n",
      "\n",
      "Epoch 00060: val_loss did not improve from 0.44367\n",
      "Epoch 61/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0638 - acc: 0.9884 - val_loss: 0.9212 - val_acc: 0.9777\n",
      "\n",
      "Epoch 00061: val_loss did not improve from 0.44367\n",
      "Epoch 62/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0497 - acc: 0.9870 - val_loss: 1.0165 - val_acc: 0.9850\n",
      "\n",
      "Epoch 00062: val_loss did not improve from 0.44367\n",
      "Epoch 63/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0976 - acc: 0.9889 - val_loss: 1.0946 - val_acc: 0.9814\n",
      "\n",
      "Epoch 00063: val_loss did not improve from 0.44367\n",
      "Epoch 64/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0569 - acc: 0.9864 - val_loss: 0.7192 - val_acc: 0.9733\n",
      "\n",
      "Epoch 00064: val_loss did not improve from 0.44367\n",
      "Epoch 65/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0744 - acc: 0.9887 - val_loss: 1.2012 - val_acc: 0.9777\n",
      "\n",
      "Epoch 00065: val_loss did not improve from 0.44367\n",
      "Epoch 66/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0484 - acc: 0.9876 - val_loss: 0.9922 - val_acc: 0.9748\n",
      "\n",
      "Epoch 00066: val_loss did not improve from 0.44367\n",
      "Epoch 67/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0388 - acc: 0.9921 - val_loss: 1.0950 - val_acc: 0.9825\n",
      "\n",
      "Epoch 00067: val_loss did not improve from 0.44367\n",
      "Epoch 68/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0602 - acc: 0.9865 - val_loss: 1.8759 - val_acc: 0.9799\n",
      "\n",
      "Epoch 00068: val_loss did not improve from 0.44367\n",
      "Epoch 69/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0761 - acc: 0.9864 - val_loss: 1.1909 - val_acc: 0.9723\n",
      "\n",
      "Epoch 00069: val_loss did not improve from 0.44367\n",
      "Epoch 70/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0925 - acc: 0.9841 - val_loss: 1.1247 - val_acc: 0.9821\n",
      "\n",
      "Epoch 00070: val_loss did not improve from 0.44367\n",
      "Epoch 71/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0529 - acc: 0.9870 - val_loss: 1.0738 - val_acc: 0.9785\n",
      "\n",
      "Epoch 00071: val_loss did not improve from 0.44367\n",
      "Epoch 72/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0822 - acc: 0.9878 - val_loss: 1.3745 - val_acc: 0.9759\n",
      "\n",
      "Epoch 00072: val_loss did not improve from 0.44367\n",
      "Epoch 73/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0483 - acc: 0.9898 - val_loss: 1.0591 - val_acc: 0.9825\n",
      "\n",
      "Epoch 00073: val_loss did not improve from 0.44367\n",
      "Epoch 74/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0843 - acc: 0.9894 - val_loss: 0.9974 - val_acc: 0.9825\n",
      "\n",
      "Epoch 00074: val_loss did not improve from 0.44367\n",
      "Epoch 75/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0495 - acc: 0.9897 - val_loss: 1.1016 - val_acc: 0.9821\n",
      "\n",
      "Epoch 00075: val_loss did not improve from 0.44367\n",
      "Epoch 76/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0353 - acc: 0.9918 - val_loss: 1.8141 - val_acc: 0.9850\n",
      "\n",
      "Epoch 00076: val_loss did not improve from 0.44367\n",
      "Epoch 77/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0643 - acc: 0.9885 - val_loss: 1.2555 - val_acc: 0.9799\n",
      "\n",
      "Epoch 00077: val_loss did not improve from 0.44367\n",
      "Epoch 78/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0664 - acc: 0.9851 - val_loss: 0.7857 - val_acc: 0.9810\n",
      "\n",
      "Epoch 00078: val_loss did not improve from 0.44367\n",
      "Epoch 79/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0321 - acc: 0.9925 - val_loss: 1.1489 - val_acc: 0.9843\n",
      "\n",
      "Epoch 00079: val_loss did not improve from 0.44367\n",
      "Epoch 80/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0413 - acc: 0.9921 - val_loss: 1.2181 - val_acc: 0.9843\n",
      "\n",
      "Epoch 00080: val_loss did not improve from 0.44367\n",
      "Epoch 81/1000\n",
      "10953/10953 [==============================] - 16s 1ms/step - loss: 0.0463 - acc: 0.9912 - val_loss: 1.1288 - val_acc: 0.9843\n",
      "\n",
      "Epoch 00081: val_loss did not improve from 0.44367\n",
      "Epoch 82/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0248 - acc: 0.9939 - val_loss: 1.6473 - val_acc: 0.9876\n",
      "\n",
      "Epoch 00082: val_loss did not improve from 0.44367\n",
      "Epoch 83/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.1427 - acc: 0.9862 - val_loss: 0.7675 - val_acc: 0.9774\n",
      "\n",
      "Epoch 00083: val_loss did not improve from 0.44367\n",
      "Epoch 84/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10953/10953 [==============================] - 15s 1ms/step - loss: 0.0273 - acc: 0.9926 - val_loss: 1.6115 - val_acc: 0.9854\n",
      "\n",
      "Epoch 00084: val_loss did not improve from 0.44367\n",
      "Epoch 85/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0569 - acc: 0.9912 - val_loss: 1.1397 - val_acc: 0.9847\n",
      "\n",
      "Epoch 00085: val_loss did not improve from 0.44367\n",
      "Epoch 86/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0461 - acc: 0.9910 - val_loss: 1.6483 - val_acc: 0.9817\n",
      "\n",
      "Epoch 00086: val_loss did not improve from 0.44367\n",
      "Epoch 87/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0482 - acc: 0.9891 - val_loss: 1.3592 - val_acc: 0.9836\n",
      "\n",
      "Epoch 00087: val_loss did not improve from 0.44367\n",
      "Epoch 88/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0560 - acc: 0.9876 - val_loss: 1.1922 - val_acc: 0.9814\n",
      "\n",
      "Epoch 00088: val_loss did not improve from 0.44367\n",
      "Epoch 89/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0357 - acc: 0.9932 - val_loss: 1.6246 - val_acc: 0.9854\n",
      "\n",
      "Epoch 00089: val_loss did not improve from 0.44367\n",
      "Epoch 90/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0427 - acc: 0.9933 - val_loss: 1.8858 - val_acc: 0.9847\n",
      "\n",
      "Epoch 00090: val_loss did not improve from 0.44367\n",
      "Epoch 91/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.1100 - acc: 0.9892 - val_loss: 0.8313 - val_acc: 0.9726\n",
      "\n",
      "Epoch 00091: val_loss did not improve from 0.44367\n",
      "Epoch 92/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0525 - acc: 0.9869 - val_loss: 1.3527 - val_acc: 0.9832\n",
      "\n",
      "Epoch 00092: val_loss did not improve from 0.44367\n",
      "Epoch 93/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0397 - acc: 0.9916 - val_loss: 1.5086 - val_acc: 0.9825\n",
      "\n",
      "Epoch 00093: val_loss did not improve from 0.44367\n",
      "Epoch 94/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0441 - acc: 0.9915 - val_loss: 1.8188 - val_acc: 0.9858\n",
      "\n",
      "Epoch 00094: val_loss did not improve from 0.44367\n",
      "Epoch 95/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0258 - acc: 0.9950 - val_loss: 1.6910 - val_acc: 0.9850\n",
      "\n",
      "Epoch 00095: val_loss did not improve from 0.44367\n",
      "Epoch 96/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0392 - acc: 0.9938 - val_loss: 1.6369 - val_acc: 0.9836\n",
      "\n",
      "Epoch 00096: val_loss did not improve from 0.44367\n",
      "Epoch 97/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.1024 - acc: 0.9862 - val_loss: 1.1969 - val_acc: 0.9755\n",
      "\n",
      "Epoch 00097: val_loss did not improve from 0.44367\n",
      "Epoch 98/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0320 - acc: 0.9913 - val_loss: 1.5541 - val_acc: 0.9832\n",
      "\n",
      "Epoch 00098: val_loss did not improve from 0.44367\n",
      "Epoch 99/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0448 - acc: 0.9921 - val_loss: 1.7271 - val_acc: 0.9806\n",
      "\n",
      "Epoch 00099: val_loss did not improve from 0.44367\n",
      "Epoch 100/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0530 - acc: 0.9891 - val_loss: 1.1708 - val_acc: 0.9865\n",
      "\n",
      "Epoch 00100: val_loss did not improve from 0.44367\n",
      "Epoch 101/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0359 - acc: 0.9930 - val_loss: 1.4208 - val_acc: 0.9858\n",
      "\n",
      "Epoch 00101: val_loss did not improve from 0.44367\n",
      "Epoch 102/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0468 - acc: 0.9926 - val_loss: 1.2379 - val_acc: 0.9861\n",
      "\n",
      "Epoch 00102: val_loss did not improve from 0.44367\n",
      "Epoch 103/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0201 - acc: 0.9961 - val_loss: 1.0419 - val_acc: 0.9890\n",
      "\n",
      "Epoch 00103: val_loss did not improve from 0.44367\n",
      "Epoch 104/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0398 - acc: 0.9913 - val_loss: 0.8626 - val_acc: 0.9869\n",
      "\n",
      "Epoch 00104: val_loss did not improve from 0.44367\n",
      "Epoch 105/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0265 - acc: 0.9944 - val_loss: 1.2987 - val_acc: 0.9796\n",
      "\n",
      "Epoch 00105: val_loss did not improve from 0.44367\n",
      "Epoch 106/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0471 - acc: 0.9919 - val_loss: 0.5971 - val_acc: 0.9799\n",
      "\n",
      "Epoch 00106: val_loss did not improve from 0.44367\n",
      "Epoch 107/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0633 - acc: 0.9898 - val_loss: 0.8519 - val_acc: 0.9828\n",
      "\n",
      "Epoch 00107: val_loss did not improve from 0.44367\n",
      "Epoch 108/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0225 - acc: 0.9949 - val_loss: 1.5188 - val_acc: 0.9843\n",
      "\n",
      "Epoch 00108: val_loss did not improve from 0.44367\n",
      "Epoch 109/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0519 - acc: 0.9911 - val_loss: 1.0829 - val_acc: 0.9839\n",
      "\n",
      "Epoch 00109: val_loss did not improve from 0.44367\n",
      "Epoch 110/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0512 - acc: 0.9916 - val_loss: 1.1181 - val_acc: 0.9770\n",
      "\n",
      "Epoch 00110: val_loss did not improve from 0.44367\n",
      "Epoch 111/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0219 - acc: 0.9948 - val_loss: 1.6072 - val_acc: 0.9839\n",
      "\n",
      "Epoch 00111: val_loss did not improve from 0.44367\n",
      "Epoch 112/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0138 - acc: 0.9966 - val_loss: 1.8928 - val_acc: 0.9883\n",
      "\n",
      "Epoch 00112: val_loss did not improve from 0.44367\n",
      "Epoch 113/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0187 - acc: 0.9967 - val_loss: 1.8774 - val_acc: 0.9887\n",
      "\n",
      "Epoch 00113: val_loss did not improve from 0.44367\n",
      "Epoch 114/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0328 - acc: 0.9953 - val_loss: 1.5708 - val_acc: 0.9883\n",
      "\n",
      "Epoch 00114: val_loss did not improve from 0.44367\n",
      "Epoch 115/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0364 - acc: 0.9957 - val_loss: 1.9037 - val_acc: 0.9883\n",
      "\n",
      "Epoch 00115: val_loss did not improve from 0.44367\n",
      "Epoch 116/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0322 - acc: 0.9946 - val_loss: 1.5251 - val_acc: 0.9858\n",
      "\n",
      "Epoch 00116: val_loss did not improve from 0.44367\n",
      "Epoch 117/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0633 - acc: 0.9917 - val_loss: 1.2821 - val_acc: 0.9817\n",
      "\n",
      "Epoch 00117: val_loss did not improve from 0.44367\n",
      "Epoch 118/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0452 - acc: 0.9893 - val_loss: 1.3679 - val_acc: 0.9810\n",
      "\n",
      "Epoch 00118: val_loss did not improve from 0.44367\n",
      "Epoch 119/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0353 - acc: 0.9932 - val_loss: 1.4447 - val_acc: 0.9847\n",
      "\n",
      "Epoch 00119: val_loss did not improve from 0.44367\n",
      "Epoch 120/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0342 - acc: 0.9940 - val_loss: 1.8577 - val_acc: 0.9869\n",
      "\n",
      "Epoch 00120: val_loss did not improve from 0.44367\n",
      "Epoch 121/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0388 - acc: 0.9924 - val_loss: 1.5565 - val_acc: 0.9847\n",
      "\n",
      "Epoch 00121: val_loss did not improve from 0.44367\n",
      "Epoch 122/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0194 - acc: 0.9956 - val_loss: 2.0227 - val_acc: 0.9876\n",
      "\n",
      "Epoch 00122: val_loss did not improve from 0.44367\n",
      "Epoch 123/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0246 - acc: 0.9958 - val_loss: 1.4987 - val_acc: 0.9850\n",
      "\n",
      "Epoch 00123: val_loss did not improve from 0.44367\n",
      "Epoch 124/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0150 - acc: 0.9962 - val_loss: 1.9345 - val_acc: 0.9887\n",
      "\n",
      "Epoch 00124: val_loss did not improve from 0.44367\n",
      "Epoch 125/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0425 - acc: 0.9942 - val_loss: 1.7754 - val_acc: 0.9861\n",
      "\n",
      "Epoch 00125: val_loss did not improve from 0.44367\n",
      "Epoch 126/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0360 - acc: 0.9948 - val_loss: 1.0493 - val_acc: 0.9883\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00126: val_loss did not improve from 0.44367\n",
      "Epoch 127/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0761 - acc: 0.9900 - val_loss: 1.2410 - val_acc: 0.9843\n",
      "\n",
      "Epoch 00127: val_loss did not improve from 0.44367\n",
      "Epoch 128/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0398 - acc: 0.9932 - val_loss: 1.0843 - val_acc: 0.9759\n",
      "\n",
      "Epoch 00128: val_loss did not improve from 0.44367\n",
      "Epoch 129/1000\n",
      "10953/10953 [==============================] - 13s 1ms/step - loss: 0.0318 - acc: 0.9921 - val_loss: 1.5868 - val_acc: 0.9843\n",
      "\n",
      "Epoch 00129: val_loss did not improve from 0.44367\n",
      "Epoch 130/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0250 - acc: 0.9943 - val_loss: 2.5123 - val_acc: 0.9869\n",
      "\n",
      "Epoch 00130: val_loss did not improve from 0.44367\n",
      "Epoch 131/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0247 - acc: 0.9955 - val_loss: 2.0615 - val_acc: 0.9865\n",
      "\n",
      "Epoch 00131: val_loss did not improve from 0.44367\n",
      "Epoch 132/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0574 - acc: 0.9938 - val_loss: 1.6535 - val_acc: 0.9843\n",
      "\n",
      "Epoch 00132: val_loss did not improve from 0.44367\n",
      "Epoch 133/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0589 - acc: 0.9911 - val_loss: 1.7345 - val_acc: 0.9803\n",
      "\n",
      "Epoch 00133: val_loss did not improve from 0.44367\n",
      "Epoch 134/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0430 - acc: 0.9923 - val_loss: 1.7298 - val_acc: 0.9854\n",
      "\n",
      "Epoch 00134: val_loss did not improve from 0.44367\n",
      "Epoch 135/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0269 - acc: 0.9939 - val_loss: 2.2553 - val_acc: 0.9869\n",
      "\n",
      "Epoch 00135: val_loss did not improve from 0.44367\n",
      "Epoch 136/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0215 - acc: 0.9956 - val_loss: 2.0523 - val_acc: 0.9869\n",
      "\n",
      "Epoch 00136: val_loss did not improve from 0.44367\n",
      "Epoch 137/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0423 - acc: 0.9942 - val_loss: 1.7516 - val_acc: 0.9817\n",
      "\n",
      "Epoch 00137: val_loss did not improve from 0.44367\n",
      "Epoch 138/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0336 - acc: 0.9932 - val_loss: 1.2029 - val_acc: 0.9843\n",
      "\n",
      "Epoch 00138: val_loss did not improve from 0.44367\n",
      "Epoch 139/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0190 - acc: 0.9956 - val_loss: 2.0747 - val_acc: 0.9876\n",
      "\n",
      "Epoch 00139: val_loss did not improve from 0.44367\n",
      "Epoch 140/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0836 - acc: 0.9967 - val_loss: 1.7770 - val_acc: 0.9883\n",
      "\n",
      "Epoch 00140: val_loss did not improve from 0.44367\n",
      "Epoch 141/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0687 - acc: 0.9934 - val_loss: 1.4211 - val_acc: 0.9828\n",
      "\n",
      "Epoch 00141: val_loss did not improve from 0.44367\n",
      "Epoch 142/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0270 - acc: 0.9946 - val_loss: 1.4004 - val_acc: 0.9890\n",
      "\n",
      "Epoch 00142: val_loss did not improve from 0.44367\n",
      "Epoch 143/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0258 - acc: 0.9942 - val_loss: 1.5101 - val_acc: 0.9880\n",
      "\n",
      "Epoch 00143: val_loss did not improve from 0.44367\n",
      "Epoch 144/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0228 - acc: 0.9953 - val_loss: 1.5131 - val_acc: 0.9865\n",
      "\n",
      "Epoch 00144: val_loss did not improve from 0.44367\n",
      "Epoch 145/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0244 - acc: 0.9942 - val_loss: 1.3808 - val_acc: 0.9894\n",
      "\n",
      "Epoch 00145: val_loss did not improve from 0.44367\n",
      "Epoch 146/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0283 - acc: 0.9960 - val_loss: 2.1249 - val_acc: 0.9880\n",
      "\n",
      "Epoch 00146: val_loss did not improve from 0.44367\n",
      "Epoch 147/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0131 - acc: 0.9975 - val_loss: 2.7363 - val_acc: 0.9880\n",
      "\n",
      "Epoch 00147: val_loss did not improve from 0.44367\n",
      "Epoch 148/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0450 - acc: 0.9955 - val_loss: 2.0716 - val_acc: 0.9828\n",
      "\n",
      "Epoch 00148: val_loss did not improve from 0.44367\n",
      "Epoch 149/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0214 - acc: 0.9955 - val_loss: 2.0879 - val_acc: 0.9858\n",
      "\n",
      "Epoch 00149: val_loss did not improve from 0.44367\n",
      "Epoch 150/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0636 - acc: 0.9911 - val_loss: 1.6804 - val_acc: 0.9832\n",
      "\n",
      "Epoch 00150: val_loss did not improve from 0.44367\n",
      "Epoch 151/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0467 - acc: 0.9921 - val_loss: 1.6339 - val_acc: 0.9880\n",
      "\n",
      "Epoch 00151: val_loss did not improve from 0.44367\n",
      "Epoch 152/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0193 - acc: 0.9954 - val_loss: 2.2254 - val_acc: 0.9898\n",
      "\n",
      "Epoch 00152: val_loss did not improve from 0.44367\n",
      "Epoch 153/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0124 - acc: 0.9974 - val_loss: 1.5707 - val_acc: 0.9890\n",
      "\n",
      "Epoch 00153: val_loss did not improve from 0.44367\n",
      "Epoch 154/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0221 - acc: 0.9959 - val_loss: 2.2435 - val_acc: 0.9890\n",
      "\n",
      "Epoch 00154: val_loss did not improve from 0.44367\n",
      "Epoch 155/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0271 - acc: 0.9970 - val_loss: 1.4893 - val_acc: 0.9890\n",
      "\n",
      "Epoch 00155: val_loss did not improve from 0.44367\n",
      "Epoch 156/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0164 - acc: 0.9960 - val_loss: 2.5916 - val_acc: 0.9898\n",
      "\n",
      "Epoch 00156: val_loss did not improve from 0.44367\n",
      "Epoch 157/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0540 - acc: 0.9953 - val_loss: 1.3821 - val_acc: 0.9847\n",
      "\n",
      "Epoch 00157: val_loss did not improve from 0.44367\n",
      "Epoch 158/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0389 - acc: 0.9932 - val_loss: 1.6600 - val_acc: 0.9890\n",
      "\n",
      "Epoch 00158: val_loss did not improve from 0.44367\n",
      "Epoch 159/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0250 - acc: 0.9957 - val_loss: 1.5779 - val_acc: 0.9883\n",
      "\n",
      "Epoch 00159: val_loss did not improve from 0.44367\n",
      "Epoch 160/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0194 - acc: 0.9958 - val_loss: 2.1272 - val_acc: 0.9901\n",
      "\n",
      "Epoch 00160: val_loss did not improve from 0.44367\n",
      "Epoch 161/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0364 - acc: 0.9944 - val_loss: 1.4284 - val_acc: 0.9865\n",
      "\n",
      "Epoch 00161: val_loss did not improve from 0.44367\n",
      "Epoch 162/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0332 - acc: 0.9951 - val_loss: 1.6564 - val_acc: 0.9876\n",
      "\n",
      "Epoch 00162: val_loss did not improve from 0.44367\n",
      "Epoch 163/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.1048 - acc: 0.9898 - val_loss: 0.8529 - val_acc: 0.9876\n",
      "\n",
      "Epoch 00163: val_loss did not improve from 0.44367\n",
      "Epoch 164/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0184 - acc: 0.9958 - val_loss: 1.3877 - val_acc: 0.9909\n",
      "\n",
      "Epoch 00164: val_loss did not improve from 0.44367\n",
      "Epoch 165/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0519 - acc: 0.9954 - val_loss: 0.9010 - val_acc: 0.9854\n",
      "\n",
      "Epoch 00165: val_loss did not improve from 0.44367\n",
      "Epoch 166/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0312 - acc: 0.9938 - val_loss: 0.9971 - val_acc: 0.9854\n",
      "\n",
      "Epoch 00166: val_loss did not improve from 0.44367\n",
      "Epoch 167/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0391 - acc: 0.9948 - val_loss: 1.3360 - val_acc: 0.9861\n",
      "\n",
      "Epoch 00167: val_loss did not improve from 0.44367\n",
      "Epoch 168/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0346 - acc: 0.9953 - val_loss: 1.2851 - val_acc: 0.9836\n",
      "\n",
      "Epoch 00168: val_loss did not improve from 0.44367\n",
      "Epoch 169/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0255 - acc: 0.9942 - val_loss: 1.6469 - val_acc: 0.9858\n",
      "\n",
      "Epoch 00169: val_loss did not improve from 0.44367\n",
      "Epoch 170/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0151 - acc: 0.9969 - val_loss: 1.8359 - val_acc: 0.9880\n",
      "\n",
      "Epoch 00170: val_loss did not improve from 0.44367\n",
      "Epoch 171/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0188 - acc: 0.9963 - val_loss: 2.5345 - val_acc: 0.9854\n",
      "\n",
      "Epoch 00171: val_loss did not improve from 0.44367\n",
      "Epoch 172/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0773 - acc: 0.9944 - val_loss: 2.1233 - val_acc: 0.9861\n",
      "\n",
      "Epoch 00172: val_loss did not improve from 0.44367\n",
      "Epoch 173/1000\n",
      "10953/10953 [==============================] - 13s 1ms/step - loss: 0.0121 - acc: 0.9976 - val_loss: 2.1626 - val_acc: 0.9854\n",
      "\n",
      "Epoch 00173: val_loss did not improve from 0.44367\n",
      "Epoch 174/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0372 - acc: 0.9970 - val_loss: 2.3758 - val_acc: 0.9836\n",
      "\n",
      "Epoch 00174: val_loss did not improve from 0.44367\n",
      "Epoch 175/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0171 - acc: 0.9962 - val_loss: 2.1255 - val_acc: 0.9869\n",
      "\n",
      "Epoch 00175: val_loss did not improve from 0.44367\n",
      "Epoch 176/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0103 - acc: 0.9984 - val_loss: 2.7239 - val_acc: 0.9872\n",
      "\n",
      "Epoch 00176: val_loss did not improve from 0.44367\n",
      "Epoch 177/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0861 - acc: 0.9942 - val_loss: 1.1675 - val_acc: 0.9821\n",
      "\n",
      "Epoch 00177: val_loss did not improve from 0.44367\n",
      "Epoch 178/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0148 - acc: 0.9959 - val_loss: 1.9416 - val_acc: 0.9861\n",
      "\n",
      "Epoch 00178: val_loss did not improve from 0.44367\n",
      "Epoch 179/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0124 - acc: 0.9981 - val_loss: 1.8773 - val_acc: 0.9858\n",
      "\n",
      "Epoch 00179: val_loss did not improve from 0.44367\n",
      "Epoch 180/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0108 - acc: 0.9984 - val_loss: 2.2447 - val_acc: 0.9869\n",
      "\n",
      "Epoch 00180: val_loss did not improve from 0.44367\n",
      "Epoch 181/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0224 - acc: 0.9974 - val_loss: 2.1782 - val_acc: 0.9880\n",
      "\n",
      "Epoch 00181: val_loss did not improve from 0.44367\n",
      "Epoch 182/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0263 - acc: 0.9974 - val_loss: 2.1714 - val_acc: 0.9869\n",
      "\n",
      "Epoch 00182: val_loss did not improve from 0.44367\n",
      "Epoch 183/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0371 - acc: 0.9941 - val_loss: 1.6163 - val_acc: 0.9843\n",
      "\n",
      "Epoch 00183: val_loss did not improve from 0.44367\n",
      "Epoch 184/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0221 - acc: 0.9956 - val_loss: 1.9415 - val_acc: 0.9876\n",
      "\n",
      "Epoch 00184: val_loss did not improve from 0.44367\n",
      "Epoch 185/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0239 - acc: 0.9960 - val_loss: 2.3579 - val_acc: 0.9869\n",
      "\n",
      "Epoch 00185: val_loss did not improve from 0.44367\n",
      "Epoch 186/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0129 - acc: 0.9976 - val_loss: 2.3174 - val_acc: 0.9901\n",
      "\n",
      "Epoch 00186: val_loss did not improve from 0.44367\n",
      "Epoch 187/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0264 - acc: 0.9958 - val_loss: 2.9430 - val_acc: 0.9894\n",
      "\n",
      "Epoch 00187: val_loss did not improve from 0.44367\n",
      "Epoch 188/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0518 - acc: 0.9959 - val_loss: 2.1597 - val_acc: 0.9865\n",
      "\n",
      "Epoch 00188: val_loss did not improve from 0.44367\n",
      "Epoch 189/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0534 - acc: 0.9935 - val_loss: 1.8391 - val_acc: 0.9810\n",
      "\n",
      "Epoch 00189: val_loss did not improve from 0.44367\n",
      "Epoch 190/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.1061 - acc: 0.9921 - val_loss: 1.5547 - val_acc: 0.9869\n",
      "\n",
      "Epoch 00190: val_loss did not improve from 0.44367\n",
      "Epoch 191/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 0.1289 - acc: 0.9939 - val_loss: 0.6744 - val_acc: 0.9796\n",
      "\n",
      "Epoch 00191: val_loss did not improve from 0.44367\n",
      "Epoch 192/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 0.0232 - acc: 0.9925 - val_loss: 1.2631 - val_acc: 0.9905\n",
      "\n",
      "Epoch 00192: val_loss did not improve from 0.44367\n",
      "Epoch 193/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0335 - acc: 0.9967 - val_loss: 1.1724 - val_acc: 0.9883\n",
      "\n",
      "Epoch 00193: val_loss did not improve from 0.44367\n",
      "Epoch 194/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 0.0344 - acc: 0.9944 - val_loss: 1.1972 - val_acc: 0.9894\n",
      "\n",
      "Epoch 00194: val_loss did not improve from 0.44367\n",
      "Epoch 195/1000\n",
      "10953/10953 [==============================] - 15s 1ms/step - loss: 0.0213 - acc: 0.9961 - val_loss: 0.9716 - val_acc: 0.9898\n",
      "\n",
      "Epoch 00195: val_loss did not improve from 0.44367\n",
      "Epoch 196/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0363 - acc: 0.9942 - val_loss: 1.0056 - val_acc: 0.9880\n",
      "\n",
      "Epoch 00196: val_loss did not improve from 0.44367\n",
      "Epoch 197/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0176 - acc: 0.9960 - val_loss: 1.3892 - val_acc: 0.9898\n",
      "\n",
      "Epoch 00197: val_loss did not improve from 0.44367\n",
      "Epoch 198/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0404 - acc: 0.9937 - val_loss: 1.0633 - val_acc: 0.9883\n",
      "\n",
      "Epoch 00198: val_loss did not improve from 0.44367\n",
      "Epoch 199/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0107 - acc: 0.9967 - val_loss: 1.8402 - val_acc: 0.9869\n",
      "\n",
      "Epoch 00199: val_loss did not improve from 0.44367\n",
      "Epoch 200/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0230 - acc: 0.9968 - val_loss: 1.9040 - val_acc: 0.9876\n",
      "\n",
      "Epoch 00200: val_loss did not improve from 0.44367\n",
      "Epoch 201/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0350 - acc: 0.9969 - val_loss: 1.9896 - val_acc: 0.9880\n",
      "\n",
      "Epoch 00201: val_loss did not improve from 0.44367\n",
      "Epoch 202/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0121 - acc: 0.9976 - val_loss: 1.5863 - val_acc: 0.9894\n",
      "\n",
      "Epoch 00202: val_loss did not improve from 0.44367\n",
      "Epoch 203/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0333 - acc: 0.9975 - val_loss: 2.5413 - val_acc: 0.9858\n",
      "\n",
      "Epoch 00203: val_loss did not improve from 0.44367\n",
      "Epoch 204/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0594 - acc: 0.9971 - val_loss: 1.6753 - val_acc: 0.9880\n",
      "\n",
      "Epoch 00204: val_loss did not improve from 0.44367\n",
      "Epoch 205/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0306 - acc: 0.9955 - val_loss: 1.9931 - val_acc: 0.9872\n",
      "\n",
      "Epoch 00205: val_loss did not improve from 0.44367\n",
      "Epoch 206/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0274 - acc: 0.9943 - val_loss: 1.7296 - val_acc: 0.9876\n",
      "\n",
      "Epoch 00206: val_loss did not improve from 0.44367\n",
      "Epoch 207/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0128 - acc: 0.9976 - val_loss: 1.6795 - val_acc: 0.9876\n",
      "\n",
      "Epoch 00207: val_loss did not improve from 0.44367\n",
      "Epoch 208/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0106 - acc: 0.9974 - val_loss: 2.1199 - val_acc: 0.9861\n",
      "\n",
      "Epoch 00208: val_loss did not improve from 0.44367\n",
      "Epoch 209/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0222 - acc: 0.9970 - val_loss: 1.8038 - val_acc: 0.9861\n",
      "\n",
      "Epoch 00209: val_loss did not improve from 0.44367\n",
      "Epoch 210/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0381 - acc: 0.9954 - val_loss: 1.8411 - val_acc: 0.9894\n",
      "\n",
      "Epoch 00210: val_loss did not improve from 0.44367\n",
      "Epoch 211/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0227 - acc: 0.9965 - val_loss: 1.6479 - val_acc: 0.9883\n",
      "\n",
      "Epoch 00211: val_loss did not improve from 0.44367\n",
      "Epoch 212/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0118 - acc: 0.9973 - val_loss: 1.1025 - val_acc: 0.9872\n",
      "\n",
      "Epoch 00212: val_loss did not improve from 0.44367\n",
      "Epoch 213/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0099 - acc: 0.9978 - val_loss: 1.6666 - val_acc: 0.9887\n",
      "\n",
      "Epoch 00213: val_loss did not improve from 0.44367\n",
      "Epoch 214/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0381 - acc: 0.9967 - val_loss: 1.2032 - val_acc: 0.9883\n",
      "\n",
      "Epoch 00214: val_loss did not improve from 0.44367\n",
      "Epoch 215/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0163 - acc: 0.9965 - val_loss: 1.7695 - val_acc: 0.9890\n",
      "\n",
      "Epoch 00215: val_loss did not improve from 0.44367\n",
      "Epoch 216/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0189 - acc: 0.9966 - val_loss: 1.8418 - val_acc: 0.9894\n",
      "\n",
      "Epoch 00216: val_loss did not improve from 0.44367\n",
      "Epoch 217/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0313 - acc: 0.9951 - val_loss: 1.8208 - val_acc: 0.9865\n",
      "\n",
      "Epoch 00217: val_loss did not improve from 0.44367\n",
      "Epoch 218/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0107 - acc: 0.9981 - val_loss: 2.4037 - val_acc: 0.9847\n",
      "\n",
      "Epoch 00218: val_loss did not improve from 0.44367\n",
      "Epoch 219/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0288 - acc: 0.9973 - val_loss: 2.1888 - val_acc: 0.9858\n",
      "\n",
      "Epoch 00219: val_loss did not improve from 0.44367\n",
      "Epoch 220/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0367 - acc: 0.9960 - val_loss: 1.6312 - val_acc: 0.9872\n",
      "\n",
      "Epoch 00220: val_loss did not improve from 0.44367\n",
      "Epoch 221/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0188 - acc: 0.9968 - val_loss: 1.9246 - val_acc: 0.9854\n",
      "\n",
      "Epoch 00221: val_loss did not improve from 0.44367\n",
      "Epoch 222/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0338 - acc: 0.9950 - val_loss: 2.1721 - val_acc: 0.9872\n",
      "\n",
      "Epoch 00222: val_loss did not improve from 0.44367\n",
      "Epoch 223/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0926 - acc: 0.9965 - val_loss: 1.2449 - val_acc: 0.9832\n",
      "\n",
      "Epoch 00223: val_loss did not improve from 0.44367\n",
      "Epoch 224/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0204 - acc: 0.9944 - val_loss: 1.9864 - val_acc: 0.9887\n",
      "\n",
      "Epoch 00224: val_loss did not improve from 0.44367\n",
      "Epoch 225/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0144 - acc: 0.9970 - val_loss: 2.0223 - val_acc: 0.9876\n",
      "\n",
      "Epoch 00225: val_loss did not improve from 0.44367\n",
      "Epoch 226/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0078 - acc: 0.9984 - val_loss: 1.8985 - val_acc: 0.9887\n",
      "\n",
      "Epoch 00226: val_loss did not improve from 0.44367\n",
      "Epoch 227/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0120 - acc: 0.9973 - val_loss: 2.2867 - val_acc: 0.9861\n",
      "\n",
      "Epoch 00227: val_loss did not improve from 0.44367\n",
      "Epoch 228/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0102 - acc: 0.9974 - val_loss: 2.7859 - val_acc: 0.9883\n",
      "\n",
      "Epoch 00228: val_loss did not improve from 0.44367\n",
      "Epoch 229/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0430 - acc: 0.9967 - val_loss: 2.0086 - val_acc: 0.9858\n",
      "\n",
      "Epoch 00229: val_loss did not improve from 0.44367\n",
      "Epoch 230/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0115 - acc: 0.9969 - val_loss: 2.6396 - val_acc: 0.9872\n",
      "\n",
      "Epoch 00230: val_loss did not improve from 0.44367\n",
      "Epoch 231/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0535 - acc: 0.9958 - val_loss: 1.9831 - val_acc: 0.9883\n",
      "\n",
      "Epoch 00231: val_loss did not improve from 0.44367\n",
      "Epoch 232/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0132 - acc: 0.9975 - val_loss: 2.8838 - val_acc: 0.9872\n",
      "\n",
      "Epoch 00232: val_loss did not improve from 0.44367\n",
      "Epoch 233/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0250 - acc: 0.9974 - val_loss: 2.8840 - val_acc: 0.9872\n",
      "\n",
      "Epoch 00233: val_loss did not improve from 0.44367\n",
      "Epoch 234/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0279 - acc: 0.9960 - val_loss: 2.6315 - val_acc: 0.9850\n",
      "\n",
      "Epoch 00234: val_loss did not improve from 0.44367\n",
      "Epoch 235/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0139 - acc: 0.9965 - val_loss: 2.5877 - val_acc: 0.9854\n",
      "\n",
      "Epoch 00235: val_loss did not improve from 0.44367\n",
      "Epoch 236/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0164 - acc: 0.9980 - val_loss: 2.8737 - val_acc: 0.9869\n",
      "\n",
      "Epoch 00236: val_loss did not improve from 0.44367\n",
      "Epoch 237/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0264 - acc: 0.9957 - val_loss: 3.1092 - val_acc: 0.9854\n",
      "\n",
      "Epoch 00237: val_loss did not improve from 0.44367\n",
      "Epoch 238/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0179 - acc: 0.9971 - val_loss: 2.4812 - val_acc: 0.9876\n",
      "\n",
      "Epoch 00238: val_loss did not improve from 0.44367\n",
      "Epoch 239/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0794 - acc: 0.9955 - val_loss: 2.2702 - val_acc: 0.9854\n",
      "\n",
      "Epoch 00239: val_loss did not improve from 0.44367\n",
      "Epoch 240/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0168 - acc: 0.9967 - val_loss: 2.3472 - val_acc: 0.9865\n",
      "\n",
      "Epoch 00240: val_loss did not improve from 0.44367\n",
      "Epoch 241/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0128 - acc: 0.9981 - val_loss: 2.4618 - val_acc: 0.9858\n",
      "\n",
      "Epoch 00241: val_loss did not improve from 0.44367\n",
      "Epoch 242/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0192 - acc: 0.9975 - val_loss: 2.8574 - val_acc: 0.9850\n",
      "\n",
      "Epoch 00242: val_loss did not improve from 0.44367\n",
      "Epoch 243/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0197 - acc: 0.9968 - val_loss: 2.0914 - val_acc: 0.9843\n",
      "\n",
      "Epoch 00243: val_loss did not improve from 0.44367\n",
      "Epoch 244/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0131 - acc: 0.9977 - val_loss: 2.2224 - val_acc: 0.9817\n",
      "\n",
      "Epoch 00244: val_loss did not improve from 0.44367\n",
      "Epoch 245/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0105 - acc: 0.9976 - val_loss: 2.6656 - val_acc: 0.9861\n",
      "\n",
      "Epoch 00245: val_loss did not improve from 0.44367\n",
      "Epoch 246/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0316 - acc: 0.9971 - val_loss: 2.4761 - val_acc: 0.9858\n",
      "\n",
      "Epoch 00246: val_loss did not improve from 0.44367\n",
      "Epoch 247/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0467 - acc: 0.9953 - val_loss: 2.6526 - val_acc: 0.9843\n",
      "\n",
      "Epoch 00247: val_loss did not improve from 0.44367\n",
      "Epoch 248/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0152 - acc: 0.9972 - val_loss: 2.9590 - val_acc: 0.9869\n",
      "\n",
      "Epoch 00248: val_loss did not improve from 0.44367\n",
      "Epoch 249/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0141 - acc: 0.9979 - val_loss: 1.9119 - val_acc: 0.9869\n",
      "\n",
      "Epoch 00249: val_loss did not improve from 0.44367\n",
      "Epoch 250/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0107 - acc: 0.9981 - val_loss: 2.1986 - val_acc: 0.9876\n",
      "\n",
      "Epoch 00250: val_loss did not improve from 0.44367\n",
      "Epoch 251/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0105 - acc: 0.9988 - val_loss: 2.1933 - val_acc: 0.9869\n",
      "\n",
      "Epoch 00251: val_loss did not improve from 0.44367\n",
      "Epoch 252/1000\n",
      "10953/10953 [==============================] - 14s 1ms/step - loss: 0.0172 - acc: 0.9976 - val_loss: 2.3620 - val_acc: 0.9869\n",
      "\n",
      "Epoch 00252: val_loss did not improve from 0.44367\n",
      "Epoch 00252: early stopping\n"
     ]
    }
   ],
   "source": [
    "model_path = 'lstm_model_classification.h5'\n",
    "class_weight = {0: 1,\n",
    "                1: 50} #2% are breaks ...\n",
    "\n",
    "history = model_class.fit(to_3D(x_train_lstm, features), df['y'].reindex(y_train.index), \n",
    "                          epochs=1000, batch_size= 8, validation_split=0.2, verbose=1, class_weight=class_weight,\n",
    "                          callbacks = [\n",
    "                              keras.callbacks.EarlyStopping(monitor='val_loss',\n",
    "                                                            min_delta=0, \n",
    "                                                            patience=200,\n",
    "                                                            verbose=1,\n",
    "                                                            mode='min'),\n",
    "\n",
    "                              keras.callbacks.ModelCheckpoint(model_path, \n",
    "                                                              monitor='val_loss',\n",
    "                                                              save_best_only=True, \n",
    "                                                              mode='min',\n",
    "                                                              verbose=1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-09T23:38:56.946229Z",
     "start_time": "2019-10-09T23:38:56.410571Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x297a06ef4e0>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8), dpi=90)\n",
    "plt.plot(model_class.history.history['val_loss'], label='val_loss')\n",
    "plt.plot(model_class.history.history['loss'], label='loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Binary cross entropy')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:18:34.103600Z",
     "start_time": "2019-10-27T15:18:33.953607Z"
    }
   },
   "outputs": [],
   "source": [
    "model_class.load_weights(\"lstm_model_classification.h5\")\n",
    "model_class.compile(loss='binary_crossentropy', optimizer='adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:18:35.382796Z",
     "start_time": "2019-10-27T15:18:34.783385Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.997     0.974     0.985      4474\n",
      "           1      0.398     0.857     0.544        91\n",
      "\n",
      "    accuracy                          0.971      4565\n",
      "   macro avg      0.697     0.915     0.764      4565\n",
      "weighted avg      0.985     0.971     0.976      4565\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print()\n",
    "print(classification_report(df['y'].reindex(y_test.index),model_class.predict_classes(to_3D(x_test_lstm, features)),digits=3))\n",
    "print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparing Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparison between different approaches: Autoencoder as anomaly detector(Chitta), Mahalanobis distance as an anomaly detector and using LSTM with class weights as a classification model, and the RUL optimized model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:18:38.033654Z",
     "start_time": "2019-10-27T15:18:37.802275Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = ['Precision', 'Recall', 'F1']\n",
    "\n",
    "# Article scores\n",
    "autoencoder = [0.045, 0.20, 0.072]\n",
    "maha_dist = [0.87, 0.56 , 0.68]\n",
    "class_weights = [0.40, 0.86, 0.54]\n",
    "rul = [0.148, 0.802, 0.250]\n",
    "\n",
    "width = 0.18  # the width of the bars\n",
    "\n",
    "# Set position of bar on X axis\n",
    "x1 = np.arange(len(labels))\n",
    "x2 = [x + width for x in x1]\n",
    "x3 = [x + width for x in x2]\n",
    "x4 = [x + width for x in x3]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12,8))\n",
    "auto_plot = ax.bar(x1, autoencoder, width, label='LSTM Autoencoder')\n",
    "maha_plot = ax.bar(x2, maha_dist, width, label='Mahalanobis Distance')\n",
    "class_weights_plot = ax.bar(x3, class_weights, width, label='LSTM Class Weights')\n",
    "rul_plot = ax.bar(x4, rul, width, label='RUL Regression Model') \n",
    "\n",
    "ax.set_ylabel('Scores', fontdict={'size':12})\n",
    "ax.set_title('Comparison between models', fontdict={'size':14})\n",
    "ax.set_xticks(np.arange(len(labels))+ 3*width/2)\n",
    "ax.set_xticklabels(labels,fontdict={'size':12})\n",
    "ax.legend()\n",
    "\n",
    "\n",
    "def autolabel(rects):\n",
    "    \"\"\"Attach a text label above each bar in *rects*, displaying its height.\"\"\"\n",
    "    for rect in rects:\n",
    "        height = rect.get_height()\n",
    "        ax.annotate('{}'.format(height),\n",
    "                    xy=(rect.get_x() + rect.get_width() / 2, height),\n",
    "                    xytext=(0, 3),  # 3 points vertical offset\n",
    "                    textcoords=\"offset points\",\n",
    "                    ha='center', va='bottom')\n",
    "\n",
    "\n",
    "autolabel(auto_plot)\n",
    "autolabel(maha_plot)\n",
    "autolabel(class_weights_plot)\n",
    "autolabel(rul_plot)\n",
    "fig.tight_layout()\n",
    "plt.show()"
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